Larval dispersal is arguably the most important but least understood demographic process in the sea. The likelihood of a larva dispersing from its birthplace to successfully recruit in another location is the culmination of many intrinsic and extrinsic factors that operate in early life. Empirically estimating the resulting population connectivity has been immensely difficult because of the challenges of studying and quantifying dispersal in the sea. Consequently, most estimates are based on predictions from biophysical models. Although there is a long history of dispersal modelling, there has been no comprehensive review of this literature. We conducted a systematic quantitative review to address the following questions: (1) Is there any bias in the distribution of research effort based on geographical or taxonomic coverage? (2) Are hydrodynamic models resolving ocean circulation at spatial scales (resolution and extent) relevant to the dispersal process under study? (3) Where, when and how many particles are being tracked, and is this effort sufficient to capture the spatiotemporal variability in dispersal? (4) How is biological and/or behavioural complexity incorporated into Lagrangian particle tracking models. (i.e. are key attributes of the dispersal process well captured.)? Our review confirms strong taxonomic and geographic biases in published work to date. We found that computational 'effort' (i.e. model resolution and particle number) has not kept pace with dramatic increases in computer processor speed. We also identified a number of shortcomings in the incorporation of biology, and behaviour specifically into models. Collectively, these findings highlight some important gaps and key areas for improvement of biophysical models that aspire to inform larval dispersal processes. In particular, we suggest the need for greater emphasis on validation of model assumptions, as well as testing of dispersal predictions with empirically derived data.

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Marine reserves are a commonly applied conservation tool, but their size is often chosen based on considerations of socioeconomic rather than ecological impact. Here, we use a simple individual-based model together with the latest empirical information on home ranges, densities and schooling behaviour in 66 coral reef fishes to quantify the conservation effectiveness of various reserve sizes. We find that standard reserves with a diameter of 1-2 km can achieve partial protection (ࣙ50% of the maximum number of individuals) of 56% of all simulated species. Partial protection of the most important fishery species, and of species with diverse functional roles, required 2-10 km wide reserves. Full protection of nearly all simulated species required 100 km wide reserves. Linear regressions based on the mean home range and density, and even just the maximum length, of fish species approximated these results reliably, and can therefore be used to support locally effective decision making.

tivity 23 1Operationalizing connectivity in spatial planning Abstract: 24 1. Globally, protected areas are being established to protect biodiversity and to promote ecosystem 25 resilience. The typical spatial conservation planning process leading to the creation of these protected 26 areas focuses on representation and replication of ecological features, often using decision support 27 systems such as Marxan. Unfortunately, Marxan currently requires manual input or specialised scripts 28 to explicitly consider ecological connectivity, a property critical to metapopulation persistence and 29 resilience. 30 2. "Marxan Connect" is a new open source, open access Graphical User Interface (GUI) designed to assist 31 conservation planners in the systematic operationalization of ecological connectivity in protected area 32 network planning.33 3. Marxan Connect is able to incorporate estimates of demographic connectivity (e.g. derived from 34tracking data, dispersal models, or genetics) or structural landscape connectivity (e.g. isolation by 35 resistance). This is accomplished by calculating metapopulation-relevant connectivity metrics (e.g. 36 eigenvector centrality) and treating those as conservation features, or using the connectivity data as a 37 spatial dependency amongst sites to be included in the prioritization process. 38 4. Marxan Connect allows a wide group of users to incorporate directional ecological connectivity into 39 conservation plans. The least-cost conservation solutions provided by Marxan Connect, combined with 40 ecologically relevant post-hoc testing, are more likely to support persistent and resilient metapopulations 41 (e.g. fish stocks) and provide better protection for biodiversity than if connectivity is ignored. 42 2Operationalizing connectivity in spatial planning 79 are needed. Knowing how to best identify, evaluate, and treat connectivity data to meet different objectives 80 within a given spatial planning framework is important to better capture key ecological processes in planning. 81Here, we outline potential workflows of realising connectivity in spatial planning, including the treatment of 82 various data formats, key decision points that link back to objectives, types of data related to connectivity, 83 3 Operationalizing connectivity in spatial planning

Larval dispersal is a key process determining population connectivity, metapopulation dynamics, and community structure in benthic marine ecosystems, yet the biophysical complexity of dispersal is not well understood. In this study, we investigate the interaction between disperser phenotype and hydrodynamics on larval dispersal pathways, using a temperate reef fish species, Trachinops caudimaculatus . We assessed the influence of larval traits on depth distribution and dispersal outcomes by: (i) using 24-h depth-stratified ichthyoplankton sampling, (ii) quantifying individual phenotypes using larval growth histories extracted from the sagittal otoliths of individual larvae, and (iii) simulating potential dispersal outcomes based on the empirical distribution of larval phenotypes and an advanced biological-physical ocean model. We found T. caudimaculatus larvae were vertically stratified with respect to phenotype, with high-quality phenotypes found in the bottom two depth strata, and poor-quality phenotypes found primarily at the surface. Our model showed high- and average-quality larvae experienced significantly higher local retention (more than double) and self-recruitment, and travelled shorter distances relative to poor-quality larvae. As populations are only connected when dispersers survive long enough to reproduce, determining how larval phenotype influences dispersal outcomes will be important for improving our understanding of marine population connectivity and persistence.