Modelling the responses of partially migratory metapopulations to changing seasonal migration rates: From theory to data

Abstract

1. Among-individual and within-individual variation in expression of seasonal migration versus residence is widespread in nature and could substantially affect the dynamics of partially migratory metapopulations inhabiting seasonally and spatially structured environments. However, such variation has rarely been explicitly incorporated into metapopulation dynamic models for partially migratory systems. We, therefore, lack general frameworks that can identify how variable seasonal movements, and associated season-and location-specific vital rates, can control system persistence.2. We constructed a novel conceptual framework that captures full-annual-cycle dynamics and key dimensions of metapopulation structure for partially migratory species inhabiting seasonal environments. We conceptualize amongindividual variation in seasonal migration as two variable vital rates: seasonal movement probability and associated movement survival probability. We conceptualize three levels of within-individual variation (i.e. plasticity), representing seasonal or annual variation in seasonal migration or lifelong fixed strategies.We formulate these concepts as a general matrix model, which is customizable for diverse life-histories and seasonal landscapes.3. To illustrate how variable seasonal migration can affect metapopulation growth rate, demographic structure and vital rate elasticities, we parameterize our general models for hypothetical short-and longer-lived species. Analyses illustrate that elasticities of seasonal movement probability and associated survival probability can sometimes equal or exceed those of vital rates typically understood to substantially influence metapopulation dynamics (i.e. seasonal survival probability or fecundity), that elasticities can vary non-linearly, and that metapopulation outcomes depend on the level of within-individual plasticity.

Eco-evolutionary dynamics of partially migratory metapopulations in spatially and seasonally varying environments

“…The overarching ambition to predict spatio-temporal population dynamic responses to rapid environmental changes requires fully understanding the eco-evolutionary impacts of spatio-seasonal environmental variation on all vital rates. In partially migratory metapopulations (PMMPs), migration probability constitutes a vital rate, causing within-population variation in space use and responses to spatio-seasonal environmental variation (Reid et al 2018, Payo-Payo et al 2022). Our eco-evolutionary model, capturing multiple subpopulations that can be linked by migration formulated as a quantitative genetic threshold trait, demonstrates how both predictable and stochastic spatial environmental variation can readily generate stable partial migration.…”

“…We envisage PMMPs in which migrants from one or more subpopulations share non-breeding season sites with residents. Such scenarios are common in nature, for example European shags in the north-eastern UK (Payo-Payo et al 2022; Acker, et al 2021), pied avocets Recurvirostra avosetta in France (Chambon et al 2018), coastal-to-inland migrations of Eurasian oystercatchers Haemotapus ostralegus in the Netherlands (Allen et al 2019), latitudinal or altitudinal migrations of songbirds such as blackcap Sylvia atricopilla (Berthold et al 1992), robin Erithacus rubicula (Biebach 1983, Adriaensen and Dhondt 1990) and European blackbird Turdus merula (Zúñiga et al 2017), ungulates such as bighorn sheep Ovis canadensis (Spitz et al 2018), fin whales Balaenoptera physalus (Geijer et al 2016) and wildebeest Connochaetes taurinus (Serneels and Lambin 2001), and among islands in tiger sharks Galeocerdo cuvier (Papastamatiou et al 2013). Given that eco-evolutionary responses to weaker ECEs than we consider (<80% mortality) will be mechanistically similar but with smaller effects, there is thus a strong empirical basis on which to postulate that the population and evolutionary dynamics we capture, including eco-evolutionary consequences of ECEs, could widely occur in nature.…”

“…PMMP structures will also alter key relationships between migration frequency and seasonal population densities, thereby altering forms and magnitudes of frequency-dependent selection on migration. Yet, while previous theory highlighted how metapopulation dynamics are complicated by the existence of migratory networks (Taylor and Norris 2010, Taylor and Hall 2012, Payo-Payo et al 2022), no work has examined spatio-seasonal eco-evolutionary dynamics in stochastic seasonal environments, including responses to ECEs to which migratory populations may be particularly vulnerable (Runge et al 2015, Shaw 2016, Kubelka et al 2022).…”

Seasonality mediates vital-rate responses to socially- and spatially-explicit density in an African lion population

“…While density can affect vital rates directly, environment-density interactions can lead to large differences in vital-rate responses to density among environmental conditions, with potentially critical effects on population persistence (Coulson et al 2001;Gamelon et al 2017). Lions in the Serengeti experience strong seasonal rainfall patterns driving prey availability (Norton-Griffiths et al 1975;Sinclair et al 2013) and these environmental patterns lead to seasonality in lion vital rates, similarly to several other systems (Letcher et al 2015;Payo-Payo et al 2022;Conquet et al 2023). However, our results additionally demonstrate that environmental seasonality can, through environment-density interactions, lead to seasonal differences in vital-rate responses to density-dependent factors.…”

The unexpected consequences of predictor error in ecological model selection

“…This hyper-exploratory ecological approach has marched in lockstep with increasing computing power, machine learning and ever-more complex and nuanced parametric statistical techniques (e.g. Bates et al ., 2009; Payo-Payo et al ., 2022; Schirmer et al ., 2023), and has revolutionised environmental science. By allowing biologists to test multiple hypotheses, model selection has allowed us to discover trends that are, from the outside, extremely difficult to identify a priori .…”