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This was indeed what our ABM generated prey time series showed: exposed to a generalist, an intermediate and a specialist predator, respectively, values of b2 from the autoregressive analyses prey time series became significantly more negative (Fig. 1a).
The influence of predators was even more obvious and clearly portrayed in the delayed autoregressive component (b2) of the prey time series.
Consequently, the model also predicts that any changes in fragmentation are expected to affect the 1+ b1 and b2 of the prey time series.
By performing standard time series analyses on prey time series, independently generated from complex, yet controllable agent-based simulations in a natural landscape [ 30], we have shown that changes in the direct and delayed components of the two-dimensional autoregressive structure of prey time series portray specifically changes in predator-prey interactions.
These results strongly support previous notions [ 2- 5, 7] of autoregressive modeling of prey time series as powerful analytical tool for disentangling direct and indirect effects of both predators and environment on long-term dynamics of prey.
Clear statistical signals of the relative importance of inter- and intraspecific interactions in natural prey time series may thus obscured by a suite of factors preventing controlled replicable analyses.
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(d) Three-dimensional periodic diagram of predator-prey-time.
We measured prey capture time from the time of prey recognition (i.e. alert stance, orientation towards prey, grasp attempt, cheliceral activity [ 25]) until prey ingestion began.
Predator release point only affected prey-finding time when prey were located on the basal leaf of 6-leafed plants; it was longer when predators were released at the top.
represent the intrinsic growth rate and density-dependent coefficient of the prey at time, respectively.
(1) where (x t)) represents the density of the prey at time t.
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CEO of Professional Science Editing for Scientists @ prosciediting.com