Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudoabsence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study showsthat ifwe do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.

Marine foundation species shifting ranges and genetic baselines

This thesis revealed the most importance factors shaping the distribution, abundance and genetic diversity of four marine foundation species. Environmental conditions, particularly sea temperatures, nutrient availability and ocean waves, played a primary role in shaping the spatial distribution and abundance of populations, acting on scales varying from tens of meters to hundreds of kilometres. Furthermore, the use of Species Distribution Models (SDMs) with biological records of occurrence and high-resolution oceanographic data, allowed predicting species distributions across time. This approach highlighted the role of climate change, particularly when extreme temperatures prevailed during glacial and interglacial periods. These results, when combined with mtDNA and microsatellite genetic variation of populations allowed inferring for the influence of past range dynamics in the genetic diversity and structure of populations. For instance, the Last Glacial Maximum produced important shifts in species ranges, leaving obvious signatures of higher genetic diversities in regions where populations persisted (i.e., refugia). However, it was found that a species’ genetic pool is shaped by regions of persistence, adjacent to others experiencing expansions and contractions. Contradicting expectations, refugia seem to play a minor role on the re(colonization) process of previously eroded populations. In addition, the available habitat area for expanding populations and the inherent mechanisms of species dispersal in occupying available habitats were also found to be fundamental in shaping the distributions of genetic diversity. However, results suggest that the high levels of genetic diversity in some populations do not rule out that they may have experienced strong genetic erosion in the past, a process here named shifting genetic baselines. Furthermore, this thesis predicted an ongoing retraction at the rear edges and extinctions of unique genetic lineages, which will impoverish the global gene pool, strongly shifting the genetic baselines in the future.

Generate disaggregated soil allocation data using a Minimum Cross Entropy Model

Montado ecosystem in the Alentejo Region, south of Portugal, has enormous agro-ecological and economics heterogeneities. A definition of homogeneous sub-units among this heterogeneous ecosystem was made, but for them is disposal only partial statistical information about soil allocation agro-forestry activities. The paper proposal is to recover the unknown soil allocation at each homogeneous sub-unit, disaggregating a complete data set for the Montado ecosystem area using incomplete information at sub-units level. The methodological framework is based on a Generalized Maximum Entropy approach, which is developed in thee steps concerning the specification of a r order Markov process, the estimates of aggregate transition probabilities and the disaggregation data to recover the unknown soil allocation at each homogeneous sub-units. The results quality is evaluated using the predicted absolute deviation (PAD) and the "Disagegation Information Gain" (DIG) and shows very acceptable estimation errors.

Assessing soil erosion due to land use change at the Alqueva reservoir surrounding area

Tese de doutoramento, Ciências do Mar, da Terra e do Ambiente, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Temporal distribution of the short-beaked common dolphin (Delphinus delphis) in the south of Madeira Island (Portugal) and relationship with oceanographic variables

Dissertação de mestrado, Biologia Marinha, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Joint inversion of geophysical and hydrologic data during water infiltration

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Geofísica), Universidade de Lisboa, Faculdade de Ciências, 2014

Planning and management of marine protected areas : methodological approaches to cope with data scarcity

Tese de doutoramento, Biologia (Biologia Marinha e Aquacultura), Universidade de Lisboa, Faculdade de Ciências, 2015

Application of Redundancy Analysis for Aerobiological Data

An aerobiological survey was conducted through five consecutive years (2006–2010) at Worcester (England).The concentration of 20 allergenic fungal spore types was measured using a 7-day volumetric spore trap. The relationship between investigated fungal spore genera and selected meteorological parameters (maximum, minimum, mean and dew point temperatures, rainfall, relative humidity, air pressure,wind direction) was examined using an ordination method(redundancy analysis) to determine which environmental factors favoured their most abundance in the air and whether it would be possible to detect similarities between different genera in their distribution pattern. Redundancy analysis provided additional information about the biology of the studied fungi through the results of the Spearman’s rank correlation. Application of the variance inflation factor in canonical correspondence analysis indicated which explanatory variables were auto-correlated and needed to be excluded from further analyses. Obtained information will be consequently implemented in the selection of factors that will be a foundation for forecasting models for allergenic fungal spores in the future.

An Analysis of Local Wind and Air Mass Directions and Their Impact on Cladosporium Distribution Using HYSPLIT and Circular Statistics

Ecological studies that examine species-environment relationships are often limited to several meteorological parameters, i.e. mean air temperature, relative humidity, precipitation, vapour pressure deficit and solar radiation. The impact of local wind, its speed and direction are less commonly investigated in aerobiological surveys mainly due to difficulties related to the employment of specific analytical tools and interpretation of their outputs. Identification of inoculum sources of economically important plant pathogens, as well as highly allergenic bioaerosols like Cladosporium species, has not been yet explored with remote sensing data and atmospheric models such as Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). We, therefore, performed an analysis of 24 h intra-diurnal cycle of Cladosporium spp. spores from an urban site in connection with both the local wind direction and overall air mass direction computed by HYSPLIT. The observational method was a volumetric air sampler of the Hirst design with 1 h time resolution and corresponding optical detection of fungal spores with light microscopy. The atmospheric modelling was done using the on-line data set from GDAS with 1° resolution and circular statistical methods. Our results showed stronger, statistically significant correlation (p ≤ 0.05) between high Cladosporium spp. spore concentration and air mass direction compared to the local wind direction. This suggested that a large fraction of the investigated fungal spores had a regional origin and must be located more than a few kilometers away from the sampling point.

Analyzing drifter designs and data gathering methods in complex estuarine environments

Senior thesis written for Oceanography 445

IMP: Imperial Metagenomics Pipeline for high-throughput sequence data

We have developed an in-house pipeline for the processing and analyses of sequence data generated during Illumina technology-based metagenomic studies of the human gut microbiota. Each component of the pipeline has been selected following comparative analysis of available tools; however, the modular nature of software facilitates replacement of any individual component with an alternative should a better tool become available in due course. The pipeline consists of quality analysis and trimming followed by taxonomic filtering of sequence data allowing reads associated with samples to be binned according to whether they represent human, prokaryotic (bacterial/archaeal), viral, parasite, fungal or plant DNA. Viral, parasite, fungal and plant DNA can be assigned to species level on a presence/absence basis, allowing – for example – identification of dietary intake of plant-based foodstuffs and their derivatives. Prokaryotic DNA is subject to taxonomic and functional analyses, with assignment to taxonomic hierarchies (kingdom, class, order, family, genus, species, strain/subspecies) and abundance determination. After de novo assembly of sequence reads, genes within samples are predicted and used to build a non-redundant catalogue of genes. From this catalogue, per-sample gene abundance can be determined after normalization of data based on gene length. Functional annotation of genes is achieved through mapping of gene clusters against KEGG proteins, and InterProScan. The pipeline is undergoing validation using the human faecal metagenomic data of Qin et al. (2014, Nature 513, 59–64). Outputs from the pipeline allow development of tools for the integration of metagenomic and metabolomic data, moving metagenomic studies beyond determination of gene richness and representation towards microbial-metabolite mapping. There is scope to improve the outputs from viral, parasite, fungal and plant DNA analyses, depending on the depth of sequencing associated with samples. The pipeline can easily be adapted for the analyses of environmental and non-human animal samples, and for use with data generated via non-Illumina sequencing platforms.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.