Geoinformatic methodologies and quantitative tools for detecting hotspots and for multicriteria ranking and prioritization: application on biodiversity monitoring and conservation

Chi ha la responsabilità di gestire un’area protetta non solo deve essere consapevole dei problemi ambientali dell’area ma dovrebbe anche avere a disposizione dati aggiornati e appropriati strumenti metodologici per esaminare accuratamente ogni singolo problema. In effetti, il decisore ambientale deve organizzare in anticipo le fasi necessarie a fronteggiare le prevedibili variazioni che subirà la pressione antropica sulle aree protette. L’obiettivo principale della Tesi è di natura metodologica e riguarda il confronto tra differenti metodi statistici multivariati utili per l’individuazione di punti critici nello spazio e per l’ordinamento degli “oggetti ambientali” di studio e quindi per l’individuazione delle priorità di intervento ambientale. L’obiettivo ambientale generale è la conservazione del patrimonio di biodiversità. L’individuazione, tramite strumenti statistici multivariati, degli habitat aventi priorità ecologica è solamente il primo fondamentale passo per raggiungere tale obiettivo. L’informazione ecologica, integrata nel contesto antropico, è un successivo essenziale passo per effettuare valutazioni ambientali e per pianificare correttamente le azioni volte alla conservazione. Un’ampia serie di dati ed informazioni è stata necessaria per raggiungere questi obiettivi di gestione ambientale. I dati ecologici sono forniti dal Ministero dell’Ambiente Italiano e provengono al Progetto “Carta della Natura” del Paese. I dati demografici sono invece forniti dall’Istituto Italiano di Statistica (ISTAT). I dati si riferiscono a due aree geografiche italiane: la Val Baganza (Parma) e l’Oltrepò Pavese e Appennino Ligure-Emiliano. L’analisi è stata condotta a due differenti livelli spaziali: ecologico-naturalistico (l’habitat) e amministrativo (il Comune). Corrispondentemente, i risultati più significativi ottenuti sono: 1. Livello habitat: il confronto tra due metodi di ordinamento e determinazione delle priorità, il metodo del Vettore Ideale e quello della Preminenza, tramite l’utilizzo di importanti metriche ecologiche come il Valore Ecologico (E.V.) e la Sensibilità Ecologica (E.S.), fornisce dei risultati non direttamente comparabili. Il Vettore Ideale, non essendo un procedimento basato sulla ranghizzazione dei valori originali, sembra essere preferibile nel caso di paesaggi molto eterogenei in senso spaziale. Invece, il metodo della Preminenza probabilmente è da preferire in paesaggi ecologici aventi un basso grado di eterogeneità intesa nel senso di differenze non troppo grandi nel E.V. ed E.S. degli habitat. 2. Livello comunale: Al fine di prendere delle decisioni gestionali ed essendo gli habitat solo delle suddivisioni naturalistiche di un dato territorio, è necessario spostare l’attenzione sulle corrispondenti unità amministrative territoriali (i Comuni). Da questo punto di vista, l’introduzione della demografia risulta essere un elemento centrale oltre che di novità nelle analisi ecologico-ambientali. In effetti, l’analisi demografica rende il risultato di cui al punto 1 molto più realistico introducendo altre dimensioni (la pressione antropica attuale e le sue tendenze) che permettono l’individuazione di aree ecologicamente fragili. Inoltre, tale approccio individua chiaramente le responsabilità ambientali di ogni singolo ente territoriale nei riguardi della difesa della biodiversità. In effetti un ordinamento dei Comuni sulla base delle caratteristiche ambientali e demografiche, chiarisce le responsabilità gestionali di ognuno di essi. Un’applicazione concreta di questa necessaria quanto utile integrazione di dati ecologici e demografici viene discussa progettando una Rete Ecologica (E.N.). La Rete cosi ottenuta infatti presenta come elemento di novità il fatto di non essere “statica” bensì “dinamica” nel senso che la sua pianificazione tiene in considerazione il trend di pressione antropica al fine di individuare i probabili punti di futura fragilità e quindi di più critica gestione.Who has the responsibility to manage a conservation zone, not only must be aware of environmental problems but should have at his disposal updated databases and appropriate methodological instruments to examine carefully each individual case. In effect he has to arrange, in advance, the necessary steps to withstand the foreseeable variations in the trends of human pressure on conservation zones. The essential objective of this Thesis is methodological that is to compare different multivariate statistical methods useful for environmental hotspot detection and for environmental prioritization and ranking. The general environmental goal is the conservation of the biodiversity patrimony. The individuation, through multidimensional statistical tools, of habitats having top ecological priority, is only the first basic step to accomplish this aim. Ecological information integrated in the human context is an essential further step to make environmental evaluations and to plan correct conservation actions. A wide series of data and information has been necessary to accomplish environmental management tasks. Ecological data are provided by the Italian Ministry of the Environment and they refer to the Map of Italian Nature Project database. The demographic data derives from the Italian Institute of Statistics (ISTAT). The data utilized regards two Italian areas: Baganza Valley and Oltrepò Pavese and Ligurian-Emilian Apennine. The analysis has been carried out at two different spatial/scale levels: ecological-naturalistic (habitat level) and administrative (Commune level). Correspondingly, the main obtained results are: 1. Habitat level: comparing two ranking and prioritization methods, Ideal Vector and Salience, through important ecological metrics like Ecological Value (E.V.) and Ecological Sensitivity (E.S.), gives results not directly comparable. Being not based on a ranking process, Ideal Vector method seems to be used preferentially in landscapes characterized by high spatial heterogeneity. On the contrary, Salience method is probably to be preferred in ecological landscapes characterized by a low degree of heterogeneity in terms of not large differences concerning habitat E.V. and E.S.. 2. Commune level: Being habitat only a naturalistic partition of a given territory, it is necessary, for management decisions, to move towards the corresponding administrative units (Communes). From this point of view, the introduction of demography is an essential element of novelty in environmental analysis. In effect, demographic analysis makes the goal at point 1 more realistic introducing other dimensions (actual human pressure and its trend) which allows the individuation of environmentally fragile areas. Furthermore this approach individuates clearly the environmental responsibility of each administrative body for what concerns the biodiversity conservation. In effect communes’ ranking, according to environmental/demographic features, clarify the responsibilities of each administrative body. A concrete application of this necessary and useful integration of ecological and demographic data has been developed in designing an Ecological Network (E.N.).The obtained E.N. has the novelty to be not “static” but “dynamic” that is the network planning take into account the demographic pressure trends in the individuation of the probable future fragile points

The total assessment profile, volume 2

Appendices are presented which include discussions of interest formulas, factors in regionalization, parametric modeling of discounted benefit-sacrifice streams, engineering economic calculations, and product innovation. For Volume 1, see

A Bayesian space-time model for discrete spread processes on a lattice

Funding for this work was provided by GEOIDE through the Government of Canada’s Networks for Centres of Excellence program.In this article we present a Bayesian Markov model for investigating environmental spread processes. We formulate a model where the spread of a disease over a heterogeneous landscape through time is represented as a probabilistic function of two processes: local diffusion and random-jump dispersal. This formulation represents two mechanisms of spread which result in highly peaked and long-tailed distributions of dispersal distances (i.e., local and long-distance spread), commonly observed in the spread of infectious diseases and biological invasions. We demonstrate the properties of this model using a simulation experiment and an empirical case study - the spread of mountain pine beetle in western Canada. Posterior predictive checking was used to validate the number of newly inhabited regions in each time period. The model performed well in the simulation study in which a goodness-of-fit statistic measuring the number of newly inhabited regions in each time interval fell within the 95% posterior predictive credible interval in over 97% of simulations. The case study of a mountain pine beetle infestation in western Canada (1999-2009) extended the base model in two ways. First, spatial covariates thought to impact the local diffusion parameters, elevation and forest cover, were included in the model. Second, a refined definition for translocation or jump-dispersal based on mountain pine beetle ecology was incorporated improving the fit of the model. Posterior predictive checks on the mountain pine beetle model found that the observed goodness-of-fit test statistic fell within the 95% posterior predictive credible interval for 8 out of 10. years. The simulation study and case study provide evidence that the model presented here is both robust and flexible; and is therefore appropriate for a wide range of spread processes in epidemiology and ecology.PostprintPeer reviewe

Nationwide forestry applications program. Analysis of forest classification accuracy

The development of LANDSAT classification accuracy assessment techniques, and of a computerized system for assessing wildlife habitat from land cover maps are considered. A literature review on accuracy assessment techniques and an explanation for the techniques development under both projects are included along with listings of the computer programs. The presentations and discussions at the National Working Conference on LANDSAT Classification Accuracy are summarized. Two symposium papers which were published on the results of this project are appended

Conditional Transformation Models

The ultimate goal of regression analysis is to obtain information about the conditional distribution of a response given a set of explanatory variables. This goal is, however, seldom achieved because most established regression models only estimate the conditional mean as a function of the explanatory variables and assume that higher moments are not affected by the regressors. The underlying reason for such a restriction is the assumption of additivity of signal and noise. We propose to relax this common assumption in the framework of transformation models. The novel class of semiparametric regression models proposed herein allows transformation functions to depend on explanatory variables. These transformation functions are estimated by regularised optimisation of scoring rules for probabilistic forecasts, e.g. the continuous ranked probability score. The corresponding estimated conditional distribution functions are consistent. Conditional transformation models are potentially useful for describing possible heteroscedasticity, comparing spatially varying distributions, identifying extreme events, deriving prediction intervals and selecting variables beyond mean regression effects. An empirical investigation based on a heteroscedastic varying coefficient simulation model demonstrates that semiparametric estimation of conditional distribution functions can be more beneficial than kernel-based non-parametric approaches or parametric generalised additive models for location, scale and shape

Eukaryotic metabarcoding pipelines for biodiversity assessment of marine benthic communities affected by ocean acidification

The development of high-throughput sequencing technologies has provided ecologists with an efficient approach to assess biodiversity in benthic communities, particularly with the recent advances in metabarcoding technologies using universal primers. However, analyzing such high-throughput data is posing important computational challenges, requiring specialized bioinformatics solutions at different stages during the processing pipeline, such as assembly of paired-end reads, chimera removal, correction of sequencing errors, and clustering of obtained sequences into Molecular Operational Taxonomic Units (MOTUs). The inferred MOTUs can then be used to estimate species diversity, composition, and richness. Although a number of methods have been developed and commonly used to cluster the sequences into MOTUs, relatively little guidance is available on their relative performance. We focused our study in the benthic community from a natural CO2 vent present in the Canary Islands, as it can be used as a natural laboratory in which to investigate the impacts of chronic ocean acidification. Here, we propose a pipeline for studying this community using a fragment of the mitochondrial cytochrome c oxidase I (COI) sequence. We compared two DNA extraction methods, two clustering methods and validated a robust method to eliminate false positives. We found that we can obtain optimal results purifying DNA from 0.3 g of sample. Using the step-by-step aggregation algorithm implemented in SWARM for clustering yields similar results as using the Bayesian clustering method of CROP, in much less time. We introduced the new algorithm MINT (Multiple Intersection of N Tags), in order to eliminate false positives due to random errors produced before or after the sequencing. Our results show that a fully-automated analysis pipeline can be used for assessing biodiversity of marine benthic communities using COI as a metabarcoding marker in an objective, accurate and affordable manner