Dynamic soaring in the winds of change: The effects of wind and oceanography on the population and spatial ecology of seabirds

Seabirds are marine top predators regarded as indicators of the environmental changes occurring in their supporting ecosystems. The analytical lens of this thesis focusses on seabird belonging to the order Procellariiformes, which have similar life-histories characterised by high life expectancy and delayed sexual maturity. Furthermore, despite acting as central place foragers during breeding, most procellariiform seabirds can perform foraging trips covering thousands of kilometres by extracting energy from the wind through a flight behaviour known as "dynamic soaring". The overarching aim of my thesis is to understand the pathways through which wind and oceanographic processes affect the demography, population dynamics, foraging ecology and spatial distribution of seabirds. Focussing on the black-browed albatross (Thalassarche melanophris) as a model organism, we developed integrated population models to investigate the effects of wind and oceanographic fluctuations on the population breeding and survival processes. By analysing a demographic database spanning nearly two decades, we found that the population breeding parameters were negatively impacted by higher sea surface temperatures and positively affected by stronger winds, presumably through bottom-up environmental processes modulating food availability and accessibility. Survival was relatively constant and was only influenced by deeper ecosystem changes acting at larger spatio-temporal scales. Furthermore, our results revealed the high sensitivity of the population to the survival rate of the poorly understood sub-adult life history stages, which comprised approximately half of the total population size. We then studied the occurrence of albatross chick mortality events not caused by predation. Our results showed that, while albatross chicks weighed less in years with warmer sea temperatures, chick malnutrition and environmentally-driven food regulation did not explain the observed patterns of mortality. Rather, nestlings mortality events unrelated to predation were clustered at small scales in time and space, suggesting that part of the pronounced inter-annual variability in albatross breeding success was modulated by the prevalence of an unidentified infectious disease. By developing state-space models, we quantified a previously hypothesised, but never empirically documented "habitat-mediated" pathway linking environmental conditions to the breeding processes of a social monogamous population. Specifically, we found a higher prevalence of divorce in challenging years characterised by warmer sea surface temperatures, documenting the direct disruptive effects of ocean warming on the social monogamous bonds of albatrosses. Our work then focussed on the hypermobile Desertas petrel (Pterodroma deserta) and Bulwer's petrel (Bulweria bulwerii) as model organisms to investigate role of winds in shaping the flight behaviour and the foraging ecology of dynamic soaring seabirds during the breeding season. Desertas petrels used favourable winds to maximise their ground speed and distance covered throughout their round-trip foraging movements, among the longest recorded in any animal. Bulwer's petrels, on the other hand, exploited the stable North Atlantic trade winds, exhibiting a striking selectivity for crosswinds and engaging in crosswind zig-zag flight throughout large sections of their tracks. Under stable winds, this strategy enabled them to maximise the distance travelled and the probability of detecting odour plumes along the round trip. Crucially, the movement patterns of these two species suggest that seabirds have a priori knowledge of the regional winds and can plan their round-trip with an expectation of predicted wind conditions and costs of flight to return back to their colony. Collectively, the findings of my thesis highlight the sensitivity of seabirds to changes in oceanographic conditions and their reliance on winds to sustain their extreme life-history. Given the accelerating pace of global change and its dramatic effects on marine ecosystems, monitoring the diagnostic responses of these "sentinels" of the global ocean and, crucially, predicting their future performance is a conservation goal of upmost importance.Falkland Islands Government – Environmental Studies Budge

The conjugacy problem in extensions of Thompson's group F

The final publication is available at Springer via http://dx.doi.org/10.1007/s11856-016-1403-9We solve the twisted conjugacy problem on Thompson’s group F. We also exhibit orbit undecidable subgroups of Aut(F), and give a proof that Aut(F) and Aut+(F) are orbit decidable provided a certain conjecture on Thompson’s group T is true. By using general criteria introduced by Bogopolski, Martino and Ventura in [5], we construct a family of free extensions of F where the conjugacy problem is unsolvable. As a byproduct of our techniques, we give a new proof of a result of Bleak–Fel’shtyn–Gonçalves in [4] showing that F has property R8, and which can be extended to show that Thompson’s group T also has property R8.Peer ReviewedPostprint (author's final draft

Evaluation of policy measures for agri-food networks in Italian rural development programmes

The agri-food sector is characterized by very heterogeneous agreements and formal and informal contracts aimed to create stable relationships among firms. In this scenario, the actors are linked by common interest in creating and distributing added value. In the network, the risk and the responsibilities are shared by the participants and the transaction costs are reduced by the presence of dynamic flows of information and knowledge. Consequently, the creation and development of agri-food networks is a main objective of regional administration in their Rural Development Plans. The article item is the presentation and the discussion of the methodology used for the evaluation of Integrated Measures Project (Progetti Integrati di Filiera, PIF) presented by firm networks and agri-food chains in Veneto. The result are demonstrated extremely interesting about the understanding of PIF. Moreover, the comparative study serve to understand the result in terms of competitive advantage and income for the farmers.agri-food networking, food-chain policy, Rural Development Programme, Agricultural and Food Policy, Q18,

What's in the box? Explaining the black-box model through an evaluation of its interpretable features

Algorithms are powerful and necessary tools behind a large part of the information we use every day. However, they may introduce new sources of bias, discrimination and other unfair practices that affect people who are unaware of it. Greater algorithm transparency is indispensable to provide more credible and reliable services. Moreover, requiring developers to design transparent algorithm-driven applications allows them to keep the model accessible and human understandable, increasing the trust of end users. In this paper we present EBAnO, a new engine able to produce prediction-local explanations for a black-box model exploiting interpretable feature perturbations. EBAnO exploits the hypercolumns representation together with the cluster analysis to identify a set of interpretable features of images. Furthermore two indices have been proposed to measure the influence of input features on the final prediction made by a CNN model. EBAnO has been preliminarily tested on a set of heterogeneous images. The results highlight the effectiveness of EBAnO in explaining the CNN classification through the evaluation of interpretable features influence

A note on empirical likelihoods derived from pairwise score functions

Pairwise likelihood functions are convenient surrogates for the ordinary likelihood, useful when the latter is too di cult or even impractical to compute. One drawback of pairwise likelihood inference is that, for a multidimensional parameter of interest, the pairwise likelihood analogue of the likelihood ratio statistic does not have the standard chi-square asymptotic distribution. Invoking the theory of unbiased estimating functions, this paper proposes and discusses a computationally and theoretically attractive approach based on the derivation of empirical likelihood functions from the pairwise scores. This approach produces alternatives to the pairwise likelihood ratio statistic, which allow reference to the usual asymptotic chi-square distribution useful when the elements of the Godambe information are troublesome to evaluate or in the presence of large datasets with relative small sample sizes. Monte Carlo studies are performed in order to assess the finite-sample performance of the proposed empirical pairwise likelihood

Scattering by PT-symmetric non-local potentials

A general formalism is worked out for the description of one-dimensional scattering by non-local separable potentials and constraints on transmission and reflection coefficients are derived in the cases of P, T, or PT invariance of the Hamiltonian. The case of a solvable Yamaguchi potential is discussed in detail.Comment: 11 page

All in a twitter: Self-tuning strategies for a deeper understanding of a crisis tweet collection

Natural disasters have become more frequent during the past 20 years due to significant climate changes. These natural events are hotly debated on social networks like Twitter and a huge amount of short text messages are continuously and promptly exchanged with personal opinions, descriptions of the natural events and their corresponding consequences. The analysis of these large and complex data could help policy-makers to better understand the event as well as to set priorities. However, the correct configuration of the tweet mining process is still challenging due to variable data distribution and the availability of a large number of algorithms with different specific parameters. The analyst need to perform a large number of experiments to identify the best configuration for the overall knowledge discovery process. Innovative, scalable, and parameter-free solutions need to be explored to streamline the analytics process. This paper presents an enhanced version of PASTA (a distributed self-tuning engine) applied to a crisis tweet collection to group a corpus of tweets into cohesive and well-separated clusters with minimal analyst intervention. Experimental results performed on real data collected during natural disasters show the effectiveness of PASTA in discovering interesting groups of correlated tweets without selecting neither the algorithms nor their parameters