High‐frequency sampling and piecewise models reshape dispersal kernels of a common reef coral

Abstract

Models of dispersal potential are required to predict connectivity between populations of sessile organisms. However, to date, such models do not allow for time‐varying rates of acquisition and loss of competence to settle and metamorphose, and permit only a limited range of possible survivorship curves. We collect high‐resolution observations of coral larval survival and metamorphosis, and apply a piecewise modeling approach that incorporates a broad range of temporally varying rates of mortality and loss of competence. Our analysis identified marked changes in competence loss and mortality rates, the timing of which implicates developmental failure and depletion of energy reserves. Asymmetric demographic rates suggest more intermediate‐range dispersal, less local retention, and less long‐distance dispersal than predicted by previously employed non‐piecewise models. Because vital rates are likely temporally asymmetric, at least for nonfeeding broadcast‐spawned larvae, piecewise analysis of demographic rates will likely yield more reliable predictions of dispersal potential.

Larvae of coastal marine invertebrates enhance their settling success or benefits of planktonic development – but not both – through vertical swimming

“…Larval mortality rates are notoriously difficult to measure in the field (Rumrill 1990, Morgan 1995, White et al 2014, but even uniform mortality rates interact with vertical swimming due to the effect of behavior on dispersal time. Furthermore, although the effects of mortality rate structure highlighted by Connolly and Baird (2010), Moneghetti et al (2019) and Meyer et al (2021a) were not evident in this study, we emphasize that mortality rate structure can have unexpected consequences and deserves greater attention in future modeling studies, especially when considering population dynamics.…”

“…Thus, we expect that our analysis would have produced similar results if other mortality schemes were considered, such as elevated mortality in the lower layer due to benthic predators. Connolly and Baird (2010) and Moneghetti et al (2019) illustrated that variable mortality throughout the larval duration (particularly in tandem with individual-level variability in the timing of competence and senescence) qualitatively alters dispersal kernels in ways that can impact population dynamical predictions. Similarly, Meyer et al (2021a) suggested that spatially variable mortality rates could influence dispersal kernels by determining how far offshore successful larvae reside prior to settling and the alongshore currents they experience.…”

“…In this setting, our results did not support a strong effect of mortality rate variability upon alongshore movement. However, it is plausible that we might have seen an effect if we had modeled alongshore movement explicitly, as was done by Connolly and Baird (2010) and Moneghetti et al (2019). Furthermore, even a weak effect of the mortality rate on the dispersal kernel can alter population dynamics when propagated through several generations.…”

Larval dispersal patterns and connectivity of Acropora on Florida’s Coral Reef and its implications for restoration

“…The majority of reefs (96%) retained at least some of their larvae, but we found some reefs that retained or self-recruited a considerable amount of larvae (top 1% retained 5.5-100% or self-recruited 8.5-99.9%). These high levels are unusual for species with a long larval development such as a Acropora (Figueiredo et al, 2013), which are known to disperse great distances due to long periods of pre-competency and retention of competency (Moneghetti et al, 2019;Figueiredo et al, 2022). These areas of the reef track likely have small-scale circulation dynamics that can trap larvae around their natal reef, such as recirculation eddies driven by the flow interactions with the topography.…”

Larval and adult traits coevolve in response to coastal oceanography to shape marine dispersal kernels

“…1–3). Researchers interested in a specific species could instead estimate this relationship by collecting data and fitting a statistical model, such as (Graham et al 2008; Connolly and Baird 2010; Moneghetti et al 2019). This new estimated function could then replace equation 3 and thus f in equation 8.…”