Comparative leaf anatomy and micromorphology of the Chilean Myrtaceae: Taxonomic and ecological implications

The family Myrtaceae in Chile comprises 26 species in 10 genera. The species occur in a diverse rangeof environments including humid temperate forests, swamps, riparian habitats and coastal xeromorphicshrublands. Most of these species are either endemic to Chile or endemic to the humid temperate forestsof Chile and Argentina. Although many taxa have very restricted distributions and are of conservationconcern, little is known about their biology and vegetative anatomy. In this investigation, we describe andcompare the leaf anatomy and micromorphology of all Chilean Myrtaceae using standard protocols forlight and scanning electron microscopy. Leaf characters described here are related to epidermis, cuticle,papillae, stomata, hairs, mesophyll, crystals, secretory cavities and vascular system. Nearly all the specieshave a typical mesophytic leaf anatomy, but some species possess xerophytic characters such as doubleepidermis, hypodermis, pubescent leaves, thick adaxial epidermis and straight epidermal anticlinal walls,which correlate with the ecological distribution of the species. This is the first report on leaf anatomyand micromorphology in most of these species. We identified several leaf characters with potential tax-onomic and ecological significance. Some combinations of leaf characters can reliably delimitate genera,while others are unique to some species. An identification key using micromorphological and anatomicalcharacters is provided to distinguish genera and species.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.