Effects of Natal Dispersal and Density-Dependence on Connectivity Patterns and Population Dynamics in a Migratory Network

Abstract

Migratory species can be visualized as occupying spatial networks with nodes representing regions and the populations that seasonally occupy them and links between seasonal subpopulations representing migratory connectivity. Connectivity is often regarded as a static property of a migratory network and visualized to evaluate the vulnerability of migratory populations to changes in specific regions. However, if the network itself is a dynamical system, its connectivity can be an output of the system that may be changed by perturbations to the network. I constructed a regulated, tripartite network population model with breeding, winter, and migration route nodes that also includes natal dispersal and in which connectivity goes to a dynamical equilibrium. I investigated how natal dispersal as well as the strength of density-dependent population regulation during breeding and non-breeding seasons affects connectivity patterns and the responses of the network population to simulated habitat loss. I found that when the population is primarily regulated by availability of habitat in only one season and natal dispersal was geographically constrained, connectivity patterns were unsymmetrical with weak (diffuse) connectivity from the non-regulating to regulating season and stronger connectivity in the other direction. Less-constrained natal dispersal always resulted in weak connectivity throughout. The overall magnitude of declines caused by habitat loss was determined by relative regulation and generally was not affected by natal dispersal although it was possible, with high natal dispersal, for loss of low-quality nodes in a non-regulating season to cause increases in network population size since the low-quality nodes were acting as an ecological trap. Although we expect that localness (i.e., the extent to which declines resulting from local winter habitat loss was concentrated in a small breeding area vs. spread across a larger area) should be predicted by connectivity, localness was in fact hugely variable and affected by both density-dependence and natal dispersal and generally quite difficult to predict from the connectivity pattern. In summary, the complexity of the system meant that visualization of a network by itself, without knowledge of the underlying processes causing connectivity patterns, often does not provide a good indication of the vulnerability of the network or individual node populations to habitat loss.

Scaling migrations to communities: An empirical case of migration network in the Arctic

“…Spatial cascades are defined as the propagation of indirect effects between remote natural systems (García-Callejas et al, 2019). For instance, perturbations on the non-breeding ground of a migratory species can affect population size at a distant breeding ground through carry-over effects (Webster et al, 2002;Norris, 2005;Taylor and Norris, 2010;Wiederholt et al, 2018;Taylor, 2019). The resulting change in breeding population size can influence both the strength of trophic interactions in communities (Jefferies et al, 2004) and the flux of matter in ecosystems (Hessen et al, 2017;Springer et al, 2018) leading to local cascading effects.…”

“…Edges from node i to node j can be directed (unidirectional) or undirected (bidirectional) and unweighted (binary) or weighted (non-binary; e.g., diet matrix). Networks have been used to study ecological interactions within communities (Dunne et al, 2002;Fortuna et al, 2010), migratory pathways at the species level (Iwamura et al, 2013;Knight et al, 2018;Taylor, 2019) and dispersal or foraging movements between set of communities (García-Callejas et al, 2019). However, we still lack a network approach that can highlight the meta-ecosystem and meta-community connections maintained by the migratory species from a focal community, while taking into account the seasonality of migrations.…”

A general theory of avian migratory connectivity

“…Knight et al, 2018; Kramer et al, 2018; Ruegg et al, 2014; Trierweiler et al, 2014) and/or exploring the consequences of migratory connectivity, particularly for population dynamics (Dolman & Sutherland, 1994; Taylor & Norris, 2010; Taylor & Stutchbury, 2016). The few studies that investigated the causes of migratory connectivity found that drivers include minimising energetic costs of migration given environmental conditions en route (Norevik et al, 2020), cultural transmission of migratory behaviour (Harrison et al, 2010), land availability (Finch et al, 2017) and natal dispersal and density‐dependent population regulations (Taylor, 2019). However, these studies were largely species specific or only used theoretical simulations.…”

“…Density dependence, particularly related to intraspecific competition for resource consumption, has been shown to shape connectivity patterns in simulated migration networks (Taylor, 2019). In particular, costs associated with mutual interference (Goss‐Custard, 1980; Somveille et al, 2018), increasing search time (Pawar et al, 2012) and territorial defence (Greenberg et al, 2010), are likely to negatively affect the density dependence of energy intake in migratory birds.…”

Habitat distribution affects connectivity and population size in migratory networks

“…We particularly expect first year birds to disperse away from their natal regions [Cresswell, 2014]. Taylor [2019] incorporated natal dispersal into a tripartite network model and found that the resulting pattern of connectivity depended on the relative strength of density-dependence in each season and the degree and constraints of dispersal. Again, these findings were determined by numerically solving difference equations rather than analytically.…”