Using larval dispersal simulations for marine protected area design: Application to the Gulf of Lions (northwest Mediterranean)

Abstract

The design (location and size) of sustaining, no-take reserves was investigated by combining realistic numerical simulations of larval dispersal from a sedentary marine species with a population dynamics model. The method explored, a priori: (1) the planktonic larval duration (PLD) of self-persistent populations within no-take reserves with radii from 1 to 20 km, (2) the size of a no-take reserve reaching self-persistent recruitment of the reserve population, and (3) offspring spillover to adjacent fisheries for PLDs from 1 to 6 weeks. In the Gulf of Lions (northwest Mediterranean), as the radius of a no-take reserve increased to 20 km, the median PLD of a selfpersistent species within the reserve increased from 2 to 6.5 d. No unique relation between PLD and sustaining notake reserve size could be established because of large spatial and temporal variabilities, thus precluding any general guidelines for marine protected area sizes. For species with mass spawning lasting , 3 d, variability due to spawning timing yielded twice the spatial variability, reflecting strong wind variability. In contrast, when spawning lasted more than 10 d, the spawning location became more important. Thus, a biological process (spawning duration) can trigger deterministic and stochastic effects of environmental variability. Finally, some unprotected areas (Narbonne to Agde and the Camargue) clearly appeared to be better locations than the existing no-take reserves for maximizing biodiversity persistence within a reasonable no-take reserve size (10 to 20 km) and for producing offspring spillover important for regional fisheries (80%).During the 10th meeting of the

Quantification of larval traits driving connectivity: the case of Corallium rubrum (L. 1758)

“…Studies on species with PLDs of 3 weeks have shown dispersal distances of several km (Moran et al 1992;Schwindt 2007), suggesting that C. rubrum larvae in similar flow conditions could disperse the same distances. However, dispersal distances cannot be inferred solely from PLD; rather, integration between PLD and transport velocities is needed (Guizien et al 2012). Due to the limitations linked to laboratory experiments, which cannot take into account different sources of mortality present in the field (e.g., predation), our results for PLD represent the maximum intrinsic survival of the larvae.…”

“…This should imply a low local retention when combined with flow motion, with the exception of caves and crevices in which local retention could be higher. According to Guizien et al (2012), a species with neutrally buoyant larvae and PLD of 2 weeks would disperse over more than 10 km in the Gulf of Lions. Thus, C. rubrum planulae should disperse and promote connectivity between distant populations.…”

Larval Fish Swimming Behavior Alters Dispersal Patterns From Marine Protected Areas in the North-Western Mediterranean Sea

“…Patterns of dispersal are also strongly influenced by the precise location and date of spawning (e.g., Guizien et al, 2012). However, in the North-Western Mediterranean Sea, the spawning areas of Sparidae, for example, are poorly known.…”

Size and spacing rules can balance conservation and fishery management objectives for marine protected areas

“…The population models analyzed are often spatially implicit (Hastings & Botsford, ; Mangel, ; Nowlis & Roberts, ) or use simple patch models or one‐dimensional linear models to represent coastlines (Halpern, Regan, Possingham, & McCarthy, ; Hastings & Botsford, ; Kaplan, Botsford, O'Farrell, Gaines, & Jorgensen, ; Moffitt, White, & Botsford, ; Pelc, Warner, Gaines, & Paris, ). MPA design studies often use symmetric dispersal kernels or simple advection models to represent larval dispersal, and these remain constant through time (Guizien, Belharet, Marsaleix, & Guarini, ; Halpern et al., ; Kaplan & Botsford, ; Lockwood, Hastings, & Botsford, ; White, Botsford, Moffitt, & Fischer, ). Yet larval connectivity patterns are driven by highly‐variable oceanographic drivers, and are therefore characterized by spatial and temporal heterogeneity (Harrison et al., ; James, Armsworth, Mason, & Bode, ; McConnaughey, Armstrong, Hickey, & Gunderson, ).…”