MPA network design based on graph network theory and emergent properties of larval dispersal

Despite the recognised effectiveness of networks of Marine Protected Areas (MPAs) as a biodiversity conservation instrument, nowadays MPA network design frequently disregards the importance of connectivity patterns. In the case of sedentary marine populations, connectivity stems not only from the stochastic nature of the physical environment that affects early-life stages dispersal, but also from the spawning stock attributes that affect the reproductive output (e.g., passive eggs and larvae) and its survivorship. Early-life stages are virtually impossible to track in the ocean. Therefore, numerical ocean current simulations coupled to egg and larval Lagrangian transport models remain the most common approach for the assessment of marine larval connectivity. Inferred larval connectivity may be different depending on the type of connectivity considered; consequently, the prioritisation of sites for marine populations' conservation might also differ. Here, we introduce a framework for evaluating and designing MPA networks based on the identification of connectivity hotspots using graph theoretic analysis. We use as a case of study a network of open-access areas and MPAs, off Mallorca Island (Spain), and test its effectiveness for the protection of the painted comber Serranus scriba. Outputs from network analysis are used to: (1) identify critical areas for improving overall larval connectivity; (2) assess the impact of species' biological parameters in network connectivity; and (3) explore alternative MPA configurations to improve average network connectivity. Results demonstrate the potential of graph theory to identify non-trivial egg/larval dispersal patterns and emerging collective properties of the MPA network which are relevant for increasing protection efficiency.Comment: 8 figures, 3 tables, 1 Supplementary material (including 4 table; 3 figures and supplementary methods

Island time and the interplay between ecology and evolution in species diversification.

Research on the dynamics of biodiversity has progressed tremendously over recent years, although in two separate directions - ecological, to determine change over space at a given time, and evolutionary, to understand change over time. Integration of these approaches has remained elusive. Archipelagoes with a known geological chronology provide an opportunity to study ecological interactions over evolutionary time. Here, I focus on the Hawaiian archipelago and summarize the development of ecological and evolutionary research; I emphasize spiders because they have attributes allowing analysis of ecological affinities in concert with diversification. Within this framework, I highlight recent insights from the island chronosequence, in particular the importance of (i) selection and genetic drift in generating diversity; (ii) fusion and fission in fostering diversification; and (iii) variability upon which selection can act. Insights into biodiversity dynamics at the nexus of ecology and evolution are now achievable by integrating new tools, in particular (i) ecological metrics (interaction networks, maximum entropy inference) across the chronosequence to uncover community dynamics and (ii) genomic tools to understand contemporaneous microevolutionary change. The work can inform applications of invasion and restoration ecology by elucidating the importance of changes in abundances, interaction strengths, and rates of evolutionary response in shaping biodiversity

Species distribution models

Species distribution models are a group of methods often used to estimate consequences of global change, to assess ecological status and for other ecological applications. The main idea behind species distribution models is that the geographical distributions of species can, to a large part, be explained by environmental factors and that species distributions therefore can be predicted in time or space. For robust and reliable applications, models need to be based on sound ecological principles, predictions need to be as accurate as possible, and model uncertainties need to be understood. Two approaches are available for modelling entire species communities: (1) each species can be modelled individually and independently of other species or (2) community information can be incorporated into the models. The first study in this thesis compares these two modelling approaches for predicting phytoplankton assemblages in lakes. The results showed that predictive accuracy was higher when species were modelled individually. The results also showed that phytoplankton can be used for model-based assessment of ecological status. This finding is important because phytoplankton is required for assessing the ecological status of European water bodies according to the European Water Framework Directive. Dispersal barriers in the landscape or limited dispersal ability of species might be a reason for species being absent from suitable habitats, and these factors might therefore affect model accuracy. The second study in this thesis examines the influence of dispersal and the spatial configuration of ecosystems on prediction accuracy of benthic invertebrate and phytoplankton distribution and assemblage composition. The results showed only a minor influence of spatial configuration and no effect of flight ability of invertebrates on model accuracy. However, the models used may partly account for dispersal constraints, since dispersal-related factors, such as lake surface area, are included as predictor variables. The result also showed that composition of littoral invertebrate assemblages was easier to predict at sites located in well-connected lake systems, possibly because the relatively unstable littoral zone necessitates a need for species to re-colonize disturbed habitats from source populations

A global database for metacommunity ecology, integrating species, traits, environment and space

The use of functional information in the form of species traits plays an important role in explaining biodiversity patterns and responses to environmental changes. Although relationships between species composition, their traits, and the environment have been extensively studied on a case-by-case basis, results are variable, and it remains unclear how generalizable these relationships are across ecosystems, taxa and spatial scales. To address this gap, we collated 80 datasets from trait-based studies into a global database for metaCommunity Ecology: Species, Traits, Environment and Space; “CESTES”. Each dataset includes four matrices: species community abundances or presences/absences across multiple sites, species trait information, environmental variables and spatial coordinates of the sampling sites. The CESTES database is a live database: it will be maintained and expanded in the future as new datasets become available. By its harmonized structure, and the diversity of ecosystem types, taxonomic groups, and spatial scales it covers, the CESTES database provides an important opportunity for synthetic trait-based research in community ecology

Potential net primary productivity in South America: application of a global model

We use a mechanistically based ecosystem simulation model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in South America. The Terrestrial Ecosystem Model (TEM) is designed to predict major carbon and nitrogen fluxes and pool sizes in terrestrial ecosystems at continental to global scales. Information from intensively studies field sites is used in combination with continental—scale information on climate, soils, and vegetation to estimate NPP in each of 5888 non—wetland, 0.5° latitude °0.5° longitude grid cells in South America, at monthly time steps. Preliminary analyses are presented for the scenario of natural vegetation throughout the continent, as a prelude to evaluating human impacts on terrestrial NPP. The potential annual NPP of South America is estimated to be 12.5 Pg/yr of carbon (26.3 Pg/yr of organic matter) in a non—wetland area of 17.0 ° 106 km2. More than 50% of this production occurs in the tropical and subtropical evergreen forest region. Six independent model runs, each based on an independently derived set of model parameters, generated mean annual NPP estimates for the tropical evergreen forest region ranging from 900 to 1510 g°m—2°yr—1 of carbon, with an overall mean of 1170 g°m—2°yr—1. Coefficients of variation in estimated annual NPP averaged 20% for any specific location in the evergreen forests, which is probably within the confidence limits of extant NPP measurements. Predicted rates of mean annual NPP in other types of vegetation ranged from 95 g°m—2°yr—1 in arid shrublands to 930 g°m@?yr—1 in savannas, and were within the ranges measured in empirical studies. The spatial distribution of predicted NPP was directly compared with estimates made using the Miami mode of Lieth (1975). Overall, TEM predictions were °10% lower than those of the Miami model, but the two models agreed closely on the spatial patterns of NPP in south America. Unlike previous models, however, TEM estimates NPP monthly, allowing for the evaluation of seasonal phenomena. This is an important step toward integration of ecosystem models with remotely sensed information, global climate models, and atmospheric transport models, all of which are evaluated at comparable spatial and temporal scales. Seasonal patterns of NPP in South America are correlated with moisture availability in most vegetation types, but are strongly influenced by seasonal differences in cloudiness in the tropical evergreen forests. On an annual basis, moisture availability was the factor that was correlated most strongly with annual NPP in South America, but differences were again observed among vegetation types. These results allow for the investigation and analysis of climatic controls over NPP at continental scales, within and among vegetation types, and within years. Further model validation is needed. Nevertheless, the ability to investigate NPP—environment interactions with a high spatial and temporal resolution at continental scales should prove useful if not essential for rigorous analysis of the potential effects of global climate changes on terrestrial ecosystems

Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives

LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future