Connectivity modelling identifies sources and sinks of coral recruitment within reef clusters.

Connectivity aids the recovery of populations following disturbances, such as coral bleaching and tropical cyclones. Coral larval connectivity is a function of physical connectivity and larval behaviour. In this study, we used OceanParcels, a particle tracking simulator, with 2D and 3D velocity outputs from a high resolution hydrodynamic-biogeochemical marine model (RECOM) to simulate the dispersal and settlement of larvae from broadcast spawning Acropora corals in the Moore Reef cluster, northern Great Barrier Reef, following the annual spawning events in 2015, 2016 and 2017. 3D velocity simulations showed 19.40-68.80% more links and sinks than those of 2D simulations. Although the patterns of connectivity among sites vary over days and years, coral larvae consistently dispersed from east to west in the cluster domain, with some sites consistently acting as sources or sinks for local larval recruitment. Results can inform coral reef intervention plans for climate change, such as the design of marine protected areas and the deployment of proposed interventions within reef clusters. For example, the wider benefits of interventions (e.g., deployment of heat adapted corals) may be optimised when deployed at locations that are a source of larvae to others within comparable habitats across the reef cluster.

Modeling herbivore functional responses causing boom-bust dynamics following predator removal.

Native biodiversity is threatened by invasive species in many terrestrial and marine systems, and conservation managers have demonstrated successes by responding with eradication or control programs. Although invasive species are often the direct cause of threat to native species, ecosystems can react in unexpected ways to their removal or reduction. Here, we use theoretical models to predict boom-bust dynamics, where the removal of predatory or competitive pressure from a native herbivore results in oscillatory population dynamics (boom-bust), which can endanger the native species' population in the short term. We simulate control activities, applied to multiple theoretical three-species Lotka-Volterra ecosystem models consisting of vegetation, a native herbivore, and an invasive predator. Based on these communities, we then develop a predictive tool that-based on relative parameter values-predicts whether control efforts directed at the invasive predator will lead to herbivore release followed by a crash. Further, by investigating the different functional responses, we show that model structure, as well as model parameters, are important determinants of conservation outcomes. Finally, control strategies that can mitigate these negative consequences are identified. Managers working in similar data-poor ecosystems can use the predictive tool to assess the probability that their system will exhibit boom-bust dynamics, without knowing exact community parameter values.

Superadditive and subadditive dynamics are not inherent to the types of interacting threat.

Species and ecosystems usually face more than one threat. The damage caused by these multiple threats can accumulate nonlinearly: either subadditively, when the joint damage of combined threats is less than the damages of both threats individually added together, or superadditively, when the joint damage is greater than the two individual damages added together. These additivity dynamics are commonly attributed to the nature of the threatening processes, but conflicting empirical observations challenge this assumption. Here, we use a theoretical model to demonstrate that the additivity of threats can change with different magnitudes of threat impacts (effect of a threat on the population parameter, like growth rate). We use a harvested single-species population model to integrate the effects of multiple threats on equilibrium abundance. Our results reveal that threats do not always display consistent additive behavior, even in simple systems. Instead, their additivity depends on the magnitudes of the impacts of two threats, and the population parameter that is impacted by each threat. In our model specifically, when multiple threats have a low impact on the growth rate of a population, they display superadditive dynamics. In contrast, threats that impact the species' carrying capacity are always additive or subadditive. These dynamics can be understood by reference to the curvature of the relationship between a given population parameter (e.g., growth) and equilibrium population size. Our results suggest that management actions can achieve amplified benefits if they target low-amplitude threats that affect the growth rate, since these will be in a superadditive phase. More generally, our results suggest that cumulative impact theory should focus more than previously on the magnitude of the impact on the population parameter, and should be cautious about attributing additive dynamics to particular threat combinations.