Extracting signals of predation and competition from paired plankton time series

Sandvik, Gunnar; Seip, Knut Lehre; Pleym, Harald

doi:10.1127/0003-9136/2003/0157-0455

Cited by 5 publications

(5 citation statements)

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“…Some of the signals identified in this study (e.g. predation signals P1, P3 and P4) have also been identified as important signals in simulated data and in timeseries data from freshwater populations of zooplankton (Sandvik et al 2003), suggesting the presence of universal signals for some interactions. In the freshwater time series data, signals of predation could have resulted from a decrease in the prey population due to increased predation.…”

Section: Discussionsupporting

confidence: 52%

“…predation signals P1, P3 and P4) have also been identified as important signals in simulated data (Sandvik et al. 2002) and in time‐series data from freshwater populations of zooplankton (Sandvik et al. 2003), suggesting the presence of universal signals for some interactions.…”

Section: Discussionmentioning

confidence: 99%

“…The first attempt to apply the AFM to real data involved analysing observational lake plankton data (from several lakes) to determine whether predation and competition could be differentiated. The results suggested the existence of unique predation and competition signals in these time‐series, but these signals were difficult to interpret (Sandvik et al. 2003).…”

Section: Introductionmentioning

confidence: 99%

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Using the angle frequency method to detect signals of competition and predation in experimental time series

et al. 2004

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Identifying interactions among organisms is central to the study of ecology. The Angle Frequency Method (AFM) allows the detection of interactions in time series data. The AFM takes pairwise data plotted in phase diagrams and identifies signals (vector directions in phase diagrams) associated with particular interactions. Using microbial experimental systems consisting of predators (bacteriophage T4) and prey/competitors (strains of Escherichia coli), we demonstrate that the AFM can identify predator-prey and competitive interactions. The level of control afforded by such microbial experimental systems allows direct tests of the utility and robustness of the AFM. Signals of predation were distinct from signals of competition, with the strongest signal of predation corresponding to the collapse of the predator population at low prey densities. Signals of competition reflected the difference in competitive strength between the superior and the inferior competitors. In addition, the effects of invasion and resource enrichment on interactions in the laboratory communities were detectable using the AFM. Our analyses support results from model simulations and analyses of lake time series by identifying similar sets of signals characteristic of predation and competition, and demonstrate that the AFM is an effective tool in rigorous studies of time series.

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Section: Discussionsupporting

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using the angle frequency method to detect signals of competition and predation in experimental time series

et al. 2004

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“…This method extracts quantitative variables that characterise species interactions. An extension of the Key Factor Method, called the Angle Frequency Method, was developed slightly laterand tested in modelling studies (Seip and Pleym 2000;Sandvik et al 2002) and observational studies (Sandvik et al 2003;Sandvik et al 2004).…”

Section: Phase Portraitsmentioning

confidence: 99%

Influence of body size on coexistence of bird species

Leyequién

Boer

Cleef

2006

Ecological Research

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Theory suggests that body size is an important factor in determining interspecific competition and, ultimately, in structuring ecological communities. However, there is a lack of pragmatic studies linking body size and interspecific competition to patterns in ecological communities. The objective of the present study was to determine the effect of body size (mass) on competitive interactions between bird pairs and to investigate the influence of food guilds. Point-counts were carried out in nine sites every month from November 2002 to November 2003 in the Cuetzalan Region, Mexico, and we used presence/absence and abundance data for the analyses. To calculate the strength of competition we used the Angle Frequency Method to extract form factors from 20 pairwise interactions. A prototype competition interaction and random pairs were also constructed. We used clustering techniques (PCA) to calculate the dissimilarity scores (distances, D) of each of the pairwise interactions to the prototype competition and random pairs and one-way ANOVA to test for differences between the means of the random and competitive pairs. The ratio in body mass (lnBM) for each of the interacting pairs was calculated, and the association between the lnBM ratio and the strength of competition (D) was tested using a Pearson product-moment correlation coefficient. To test for the influence of foraging guilds we used a univariate general linear model. Our results demonstrate a significant negative relationship between bird body mass ratio and competition strength -i.e. competition strength increased when the body masses of the birds became more similar. We did not find a significant influence of foraging guild on the relationship between body mass ratio and competition strength. On the basis of these results, we suggest that high variation in body sizes amongst sympatric species promotes coexistence in communities.

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“…This approach has some advantages over strictly experimental approaches, such as species removals: in principle, the interaction strengths between all species pairs in a community can be estimated with one experiment (rather than requiring a comprehensive set of single-species removals), and estimates of direct pairwise interaction strengths are not confounded by indirect effects [9]. Recent applications include tide pool fish communities [11], freshwater plankton communities [12–14], the wolf-moose interaction in Isle Royale National Park [15,16], forest insects [17–19], and laboratory systems using microbes [20–24] and insects [25]. …”

Section: Introductionmentioning

confidence: 99%

Cryptic Population Dynamics: Rapid Evolution Masks Trophic Interactions

et al. 2007

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Trophic relationships, such as those between predator and prey or between pathogen and host, are key interactions linking species in ecological food webs. The structure of these links and their strengths have major consequences for the dynamics and stability of food webs. The existence and strength of particular trophic links has often been assessed using observational data on changes in species abundance through time. Here we show that very strong links can be completely missed by these kinds of analyses when changes in population abundance are accompanied by contemporaneous rapid evolution in the prey or host species. Experimental observations, in rotifer-alga and phage-bacteria chemostats, show that the predator or pathogen can exhibit large-amplitude cycles while the abundance of the prey or host remains essentially constant. We know that the species are tightly linked in these experimental microcosms, but without this knowledge, we would infer from observed patterns in abundance that the species are weakly or not at all linked. Mathematical modeling shows that this kind of cryptic dynamics occurs when there is rapid prey or host evolution for traits conferring defense against attack, and the cost of defense (in terms of tradeoffs with other fitness components) is low. Several predictions of the theory that we developed to explain the rotifer-alga experiments are confirmed in the phage-bacteria experiments, where bacterial evolution could be tracked. Modeling suggests that rapid evolution may also confound experimental approaches to measuring interaction strength, but it identifies certain experimental designs as being more robust against potential confounding by rapid evolution.

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Extracting signals of predation and competition from paired plankton time series

Cited by 5 publications

References 0 publications

Using the angle frequency method to detect signals of competition and predation in experimental time series

Using the angle frequency method to detect signals of competition and predation in experimental time series

Influence of body size on coexistence of bird species

Cryptic Population Dynamics: Rapid Evolution Masks Trophic Interactions

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