Where do you think you’re going? Accounting for ontogenetic and climate‐induced movement in spatially stratified integrated population assessment models

Abstract

Understanding spatial population structure and biocomplexity is critical for determining a species' resilience to environmental and anthropogenic perturbations. However, integrated population models (IPMs) used to develop management advice for harvested populations have been slow to incorporate spatial dynamics. Therefore, limited research has been devoted to understanding the reliability of movement parameter estimation in spatial population models, especially for spatially dynamic marine fish populations. We implemented a spatial simulation-estimation framework that emulated a generic marine fish metapopulation to explore the impact of ontogenetic movement and climate-induced distributional shifts between two populations. The robustness of spatially stratified IPMs was explored across a range of movement parametrizations, including ignoring connectivity or estimating movement with various levels of complexity. Ignoring connectivity was detrimental to accurate estimation of population-specific biomass, while implementing spatial IPMs with intermediate levels of complexity (e.g. estimating movement in two-year and two-age blocks) performed best when no a priori information about underlying movement was available. One-way distributional shifts mimicking climate-induced poleward migrations presented the greatest estimation difficulties, but the incorporation of auxiliary information on connectivity (e.g. tag-recapture data) reduced bias. The continued development of spatially stratified modelling approaches should allow harvested resources to be better utilized without increased risk. Additionally, expanded collection and incorporation of unique spatially explicit data will enhance the robustness of IPMs in the future.

Finding the perfect mismatch: Evaluating misspecification of population structure within spatially explicit integrated population models

“…Ignoring age-based movement in the spatially explicit IPMs led to biased area-specific parameter estimates. These results support previous research (Cadrin et al, 2019;Goethel et al, 2015bGoethel et al, , 2021Lee et al, 2017;Ying et al, 2011), which suggests that simplified movement dynamics can be as detrimental to spatial IPM performance as ignoring movement altogether. Estimating the full complexity of movement is intractable and movement dynamics are often as uncertain as population structure.…”

“…Our results provide further support for the general findings that suggest that spatial IPMs are likely to be more robust than spatially aggregated or panmictic IPMs even when limited understanding of underlying spatial dynamics exist (e.g., Goethel et al, 2015aGoethel et al, , 2021Punt, 2019aPunt, , 2019bYing et al, 2011). Although assuming a unit population provided unbiased estimates of system level parameters, the potential for localized depletion when subpopulation structure F I G U R E 3 Relative percent difference (RPD) between true and estimated values for F and SSB in the terminal year.…”

“…By explicitly modeling spatial dynamics (e.g. spatial variation in demographics and connectivity among spawning populations), spatially explicit IPMs can match the local scale of each data set while identifying regional scale temporal changes in species distributions (Goethel et al, 2021).…”

“…Despite more fine-scale dynamics being likely in many species (e.g. contingent structure; Petitgas et al, 2010), the parsimonycomplexity tradeoff, as well as limitations to the spatial scale of fisheries data, limits the number of population components and spatial areas that can be modelled in an IPM (Punt et al, 2018;Goethel et al, 2021;although, see Cao et al, 2020, for a spatiotemporal approach that uses spatial autocorrelation to model population dynamics at an extremely fine-scale). It has been widely demonstrated that assuming homogenous populations or applying spatially aggregated IPMs may be detrimental to achieving sustainable management of marine populations (e.g.…”

“…It has been widely demonstrated that assuming homogenous populations or applying spatially aggregated IPMs may be detrimental to achieving sustainable management of marine populations (e.g. Goethel et al, 2021;McGilliard et al, 2015;Ying et al, 2011). Yet, aside from assuming population homogeneity when metapopulation or natal homing structure is present (e.g., Li et al, 2015Li et al, , 2018Ying et al, 2011),…”