Difference Inadaptive Dispersal Ability Can Promote Species Coexistence in Fluctuating Environments

<div><p>Theories and empirical evidence suggest that random dispersal of organisms promotes species coexistence in spatially structured environments. However, directed dispersal, where movement is adjusted with fitness-related cues, is less explored in studies of dispersal-mediated coexistence. Here, we present a metacommunity model of two consumers exhibiting directed dispersal and competing for a single resource. Our results indicated that directed dispersal promotes coexistence through two distinct mechanisms, depending on the adaptiveness of dispersal. Maladaptive directed dispersal may promote coexistence similar to random dispersal. More importantly, directed dispersal is adaptive when dispersers track patches of increased resources in fluctuating environments. Coexistence is promoted under increased adaptive dispersal ability of the inferior competitor relative to the superior competitor. This newly described dispersal-mediated coexistence mechanism is likely favored by natural selection under the trade-off between competitive and adaptive dispersal abilities.</p> </div

Adaptiveness of dispersal.

<p>Adaptiveness was calculated as the difference between the invader fitness of each dispersing population of the inferior and that of their sedentary counterpart. Boundaries of positive and negative values are marked by lines. Note that the color scales are different for different dispersal modes. Parameters are (<i>k</i>₁, <i>k</i>₂) = (0.6, 0.3) for Environment 1 in (A, D, G), (0.8, 0.4) for Environment 2 in (B, E, H), and (1.4, 0.7) for Environment 3 in (C, F, I).</p

The effect of the superior consumer moving capacity (dmax,S) on resource dynamics.

<p>The effect of the superior consumer moving capacity (dmax,S) on resource dynamics when the inferior consumer is absent. These are the resource dynamics that the inferior will face as they invade the community where only the superior resides. Each panel shows the average resource level in patch 1 (, bold lines; for all four cases), average ideal resource level (, dotted lines), and the potential advantage of fitness-dependent dispersal (, shaded).</p

Summarizing the conditions and mechanisms of the two coexistence mechanisms.

<p>Summarizing the conditions and mechanisms of the two coexistence mechanisms.</p

Competition outcomes between two consumer species, depending on moving capacity.

<p>In each panel, the species compositions are denoted as follows: S: superior species dominance, Co: coexistence, and I: inferior species dominance. Stable steady states are indicated by (*), and unstable, periodic, or fluctuation outcomes are indicated by (^∧). Parameters are (<i>k</i>₁, <i>k</i>₂) = (0.6, 0.3) for Environment 1 in (A, D, G), (0.8, 0.4) for Environment 2 in (B, E, H), and (1.4, 0.7) for Environment 3 in (C, F, I).</p

Population dynamics in environment 2 under fitness-dependent dispersal.

<p>Population dynamics of consumers (the upper panel, A) and the resource (the lower panel, B) in environment 2 (<i>k</i>₁ = 0.8, <i>k</i>₂ = 0.4) under fitness-dependent dispersal. (Bold solid line: <i>R</i>₁;thin solid line: <i>R</i>₂; bold dotted line: <i>C_S</i>_,1;thin dotted line: <i>C_S</i>_,2;bold dashed line: <i>C_I</i>_,1; thin dashed line: <i>C_I</i>_,2). The superior consumer moving capacity is low (<i>d</i>_max,<i>S</i> = 0.01), and the inferior consumer moving capacity is high (<i>d</i>_max,<i>I</i> = 100).</p

R code to generate random residual x lake matrix

An example by using simulated data to generate 100 random residual x lake matrices from an observed matrix

Size-specific residuals of size spectra

Data representing the residuals of each of the size classes (columns) from fish size spectra of 74 German lakes (rows)

ESM1 from Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress

Populations occasionally experience abrupt changes, such as local extinctions, strong declines in abundance or transitions from stable dynamics to strongly irregular fluctuations. Although most of these changes have important ecological and at times economic implications, they remain notoriously difficult to detect in advance. Here, we study changes in the stability of populations under stress across a variety of transitions. Using a Ricker-type model, we simulate shifts from stable point equilibrium dynamics to cyclic and irregular boom–bust oscillations as well as abrupt shifts between alternative attractors. Our aim is to infer the loss of population stability before such shifts based on changes in nonlinearity of population dynamics. We measure nonlinearity by comparing forecast performance between linear and nonlinear models fitted on reconstructed attractors directly from observed time series. We compare nonlinearity to other suggested leading indicators of instability (variance and autocorrelation). We find that nonlinearity and variance increase in a similar way prior to the shifts. By contrast, autocorrelation is strongly affected by oscillations. Finally, we test these theoretical patterns in datasets of fisheries populations. Our results suggest that elevated nonlinearity could be used as additional indicator to infer changes in the dynamics of populations under stress

Map showing the three sampling stations in the East China Sea.

<p>Copepod nauplii and copepodites samples were respectively collected with 50 and 100 μm zooplankton net at 10 m depth at station 1, 5 and 9 in May, 2013.</p