Distribution of the common lizard (Zootoca vivipara) and landscape favourability for the species in Northern Ireland

The common lizard (Zootoca vivipara) is Ireland’s only native reptile, forming a key part of the island’s biodiversity. However, there is a general paucity of distributional and abundance data for the species. In this study, we collated incidental records for common lizard sightings to define the distribution of the species in Northern Ireland. Maximum entropy modelling was employed to describe species-habitat associations. The resulting predicted landscape favourability was used to evaluate the current status of the species based on the distribution of its maximum potential range in relation to the degree of fragmentation of remaining suitable habitat. In common with previous studies in the Republic of Ireland, sightings were highly clustered indicating under-recording, observer bias, and fragmentation of suitable habitat. A total of 98 records were collated from 1905 to 2009. The species was recorded in 63 (ca. 34%) of 186 × 10 km Northern Irish grid squares. Lizard occurrence was strongly and positively associated with landscapes dominated by heathland, bog and coastal habitats. The single best approximating model correctly classified the presence of lizards in 84.2% of cases. Upland heath, lowland raised bog and sand dune systems are all subject to Habitat Action Plans in Northern Ireland and are threatened by conversion to agriculture, afforestation, invasive species encroachment and infrastructural development. Consequently, remaining common lizard populations are likely to be small, isolated and highly fragmented. Establishment of an ecological network to preserve connectivity of remaining heath and bog will not only benefit remaining common lizard populations but biodiversity in general.

Applying species distribution modelling to identify areas of high conservation value for endangered species: A case study using Margaritifera margaritifera (L.)

The development and implementation of a population supplementation and restoration plan for any endangered species should involve an understanding of the species’ habitat requirements prior to the release of any captive bred individuals. The freshwater pearl mussel, Margaritifera margaritifera, has undergone dramatic declines over the last century and is now globally endangered. In Northern Ireland, the release of captive bred individuals is being used to support wild populations and repatriate the species in areas where it once existed. We employed a combination of maximum entropy modelling (MAXENT) and Generalized Linear Mixed Models (GLMM) to identify ecological parameters necessary to support wild populations using GIS-based landscape scale and ground-truthed habitat scale environmental parameters. The GIS-based landscape scale model suggested that mussel occurrence was associated with altitude and soil characteristics including the carbon, clay, sand, and silt content. Notably, mussels were associated with a relatively narrow band of variance indicating that M. margaritifera has a highly specific landscape niche. The ground-truthed habitat scale model suggested that mussel occurrence was associated with stable consolidated substrates, the extent of bankside trees, presence of indicative macrophyte species and fast flowing water. We propose a three phase conservation strategy for M. margaritifera identifying suitable areas within rivers that (i) have a high conservation value yet needing habitat restoration at a local level, (ii) sites for population supplementation of existing populations and (iii) sites for species reintroduction to rivers where the mussel historically occurred but is now locally extinct. A combined analytical approach including GIS-based landscape scale and ground-truthed habitat scale models provides a robust method by which suitable release sites can be identified for the population supplementation and restoration of an endangered species. Our results will be highly influential in the future management of M. margaritifera in Northern Ireland.

Simultaneous optical and X-ray observations of flares and rotational modulation on the RS CVn binary HR 1099 (V711 Tau) from the MUSICOS 1998 campaign

We present simultaneous and continuous observations of the Halpha, Hbeta, He I D-3, Na I D-1,D-2 doublet and the Ca II H&K lines for the RS CVn system HR 1099. The spectroscopic observations were obtained during the MUSICOS 1998 campaign involving several observatories and instruments, both echelle and long-slit spectrographs. During this campaign, HR 1099 was observed almost continuously for more than 8 orbits of 2.(d)8. Two large optical flares were observed, both showing an increase in the emission of Halpha, Ca II H K, Hbeta and He I D-3 and a strong filling-in of the Na I D-1, D-2 doublet. Contemporary photometric observations were carried out with the robotic telescopes APT-80 of Catania and Phoenix-25 of Fairborn Observatories. Maps of the distribution of the spotted regions on the photosphere of the binary components were derived using the Maximum Entropy and Tikhonov photometric regularization criteria. Rotational modulation was observed in Halpha and He I D-3 in anti-correlation with the photometric light curves. Both flares occurred at the same binary phase (0.85), suggesting that these events took place in the same active region. Simultaneous X-ray observations, performed by ASM on board RXTE, show several flare-like events, some of which correlate well with the observed optical flares. Rotational modulation in the X-ray light curve has been detected with minimum flux when the less active G5 V star was in front. A possible periodicity in the X-ray flare-like events was also found.

Predicting the impacts of climate change on the distribution of threatened forest-restricted birds in Madagascar

The greatest common threat to birds in Madagascar has historically been from anthropogenic deforestation. During recent decades, global climate change is now also regarded as a significant threat to biodiversity. This study uses Maximum Entropy species distribution modeling to explore how potential climate change could affect the distribution of 17 threatened forest endemic bird species, using a range of climate variables from the Hadley Center's HadCM3 climate change model, for IPCC scenario B2a, for 2050. We explore the importance of forest cover as a modeling variable and we test the use of pseudo-presences drawn from extent of occurrence distributions. Inclusion of the forest cover variable improves the models and models derived from real-presence data with forest layer are better predictors than those from pseudo-presence data. Using real-presence data, we analyzed the impacts of climate change on the distribution of nine species. We could not predict the impact of climate change on eight species because of low numbers of occurrences. All nine species were predicted to experience reductions in their total range areas, and their maximum modeled probabilities of occurrence. In general, species range and altitudinal contractions follow the reductive trend of the Maximum presence probability. Only two species (Tyto soumagnei and Newtonia fanovanae) are expected to expand their altitude range. These results indicate that future availability of suitable habitat at different elevations is likely to be critical for species persistence through climate change. Five species (Eutriorchis astur, Neodrepanis hypoxantha, Mesitornis unicolor, Euryceros prevostii, and Oriola bernieri) are probably the most vulnerable to climate change. Four of them (E. astur, M. unicolor, E. prevostii, and O. bernieri) were found vulnerable to the forest fragmentation during previous research. Combination of these two threats in the future could negatively affect these species in a drastic way. Climate change is expected to act differently on each species and it is important to incorporate complex ecological variables into species distribution models.

Identifying optimal sites for natural recovery and restoration of impacted biogenic habitats in a special area of conservation using hydrodynamic and habitat suitability modelling

Selection of sites for successful restoration of impacted shellfish populations depends on understanding the dispersion capability and habitat requirements of the species involved. In Strangford Lough, Northern Ireland, the horse mussel (Modiolus modiolus) biogenic reefs cover only a fraction of their historical range with the remaining reefs badly damaged and requiring restoration. Previous experimental trials suggest that translocation of horse mussels accelerates reef recovery and has therefore been proposed as a suitable restoration technique. We used a series of coupled hydrodynamic and particle dispersal models to assess larval dispersion from remnant and translocated populations to identify suitable areas for adult live M. modiolus translocation in Strangford Lough, Northern Ireland. A maximum entropy model (MAXENT) was used to identify if dispersing larvae could reach habitat suitable for adult M. modiolus. From these we predicted if translocated mussels will reseed themselves or be able to act as larval sources for nearby reefs. The dispersal models showed that the remnant M. modiolus populations are largely self-recruiting with little connectivity between them. The majority of larvae settled near the sources and movement was largely dependent on the tides and not influenced by wind or waves. Higher reef elevation resulted in larvae being able to disperse further away from the release point. However, larval numbers away from the source population are likely to be too low for successful recruitment. There was also little connectivity between the Irish Sea and Strangford Lough as any larvae entering the Lough remained predominantly in the Strangford Narrows. The areas covered by these self-seeding populations are suitable for M. modiolus translocation according to the MAXENT model. As a result of this work and in conjunction with other field work we propose a combination of total protection of all remaining larval sources and small scale translocations onto suitable substrata in each of the identified self-recruiting areas.

Imprecise probabilistic query answering using measures of ignorance and degree of satisfaction

In conditional probabilistic logic programming, given a query, the two most common forms for answering the query are either a probability interval or a precise probability obtained by using the maximum entropy principle. The former can be noninformative (e.g.,interval [0; 1]) and the reliability of the latter is questionable when the priori knowledge isimprecise. To address this problem, in this paper, we propose some methods to quantitativelymeasure if a probability interval or a single probability is sufficient for answering a query. We first propose an approach to measuring the ignorance of a probabilistic logic program with respect to a query. The measure of ignorance (w.r.t. a query) reflects howreliable a precise probability for the query can be and a high value of ignorance suggests that a single probability is not suitable for the query. We then propose a method to measure the probability that the exact probability of a query falls in a given interval, e.g., a second order probability. We call it the degree of satisfaction. If the degree of satisfaction is highenough w.r.t. the query, then the given interval can be accepted as the answer to the query. We also prove our measures satisfy many properties and we use a case study to demonstrate the significance of the measures. © Springer Science+Business Media B.V. 2012

Identifying optimal feeding habitat and proposed Marine Protected Areas (pMPAs) for the black-legged kittiwake (Rissa tridactyla) suggests a need for complementary management approaches

Marine Protected Areas (MPAs) are an important conservation tool. For marine predators, recent research has focused on the use of Species Distribution Models (SDMs) to identify proposed sites. We used a maximum entropy modelling approach based on static and dynamic oceanographic parameters to determine optimal feeding habitat for black-legged kittiwakes (Rissa tridactyla) at two colonies during two consecutive breeding seasons (2009 and 2010). A combination of Geographic Positioning System (GPS) loggers and Time-Depth Recorders (TDRs) attributed feeding activity to specific locations. Feeding areas were <30 km from the colony, <40 km from land, in productive waters, 25–175m deep. The predicted extent of optimal habitat declined at both colonies between 2009 and 2010 coincident with declines in reproductive success. Whilst the area of predicted optimal habitat changed, its location was spatially stable between years. There was a close match between observed feeding locations and habitat predicted as optimal at one colony (Lambay Island, Republic of Ireland), but a notable mismatch at the other (Rathlin Island, Northern Ireland). Designation of an MPA at Rathlin may, therefore, be less effective than a similar designation at Lambay perhaps due to the inherent variability in currents and sea state in the North Channel compared to the comparatively stable conditions in the central Irish Sea. Current strategies for designating MPAs do not accommodate likely future redistribution of resources due to climate change. We advocate the development of new approaches including dynamic MPAs that track changes in optimal habitat and non-colony specific ecosystem management.

Improving parameter learning of Bayesian nets from incomplete data

This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy approach and a Bayesian model averaging approach. Both ideas can be easily applied on top of EM, while the entropy idea can be also implemented in a more sophisticated way, through a dedicated non-linear solver. A vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum (penalized) log-likelihood and maximum a posteriori estimates. In particular, if EM is adopted as optimization engine, the model averaging approach is the best performing one; its performance is matched by the entropy approach when implemented using the non-linear solver. The results suggest that the applicability of these ideas is immediate (they are easy to implement and to integrate in currently available inference engines) and that they constitute a better way to learn Bayesian network parameters.

A desagregação espacial de dados agrícolas: aplicação de uma abordagem baseada na máxima entropia ao concelho de Castelo de Vide

Dissertação mest., Gestão Sustentável de Espaços Rurais, Universidade do Algarve, 2009

Habitat predictive modelling of demersal fish species in the Azores

Dissertação de Mestrado, Estudos Integrados dos Oceanos, 25 de Março de 2013, Universidade dos Açores.

An anonymizable entity finder in judicial decisions

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Utilisation de représentations de mots pour l’étiquetage de rôles sémantiques suivant FrameNet

Dans la sémantique des cadres de Fillmore, les mots prennent leur sens par rapport au contexte événementiel ou situationnel dans lequel ils s’inscrivent. FrameNet, une ressource lexicale pour l’anglais, définit environ 1000 cadres conceptuels, couvrant l’essentiel des contextes possibles. Dans un cadre conceptuel, un prédicat appelle des arguments pour remplir les différents rôles sémantiques associés au cadre (par exemple : Victime, Manière, Receveur, Locuteur). Nous cherchons à annoter automatiquement ces rôles sémantiques, étant donné le cadre sémantique et le prédicat. Pour cela, nous entrainons un algorithme d’apprentissage machine sur des arguments dont le rôle est connu, pour généraliser aux arguments dont le rôle est inconnu. On utilisera notamment des propriétés lexicales de proximité sémantique des mots les plus représentatifs des arguments, en particulier en utilisant des représentations vectorielles des mots du lexique.

A concept of approximated densities for efficient nonlinear estimation

This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD). The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques, the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy principle subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes theorem. Through simulation on a generic exponential model, the proposed nonlinear filter is implemented and the results prove to be superior to that of the extended Kalman filter and a class of nonlinear filters based on partitioning algorithms.

Generalized beta generated distributions

This article introduces generalized beta-generated (GBG) distributions. Sub-models include all classical beta-generated, Kumaraswamy-generated and exponentiated distributions. They are maximum entropy distributions under three intuitive conditions, which show that the classical beta generator skewness parameters only control tail entropy and an additional shape parameter is needed to add entropy to the centre of the parent distribution. This parameter controls skewness without necessarily differentiating tail weights. The GBG class also has tractable properties: we present various expansions for moments, generating function and quantiles. The model parameters are estimated by maximum likelihood and the usefulness of the new class is illustrated by means of some real data sets.

Species distribution models for crop pollination: a modelling framework applied to Great Britain

Insect pollination benefits over three quarters of the world's major crops. There is growing concern that observed declines in pollinators may impact on production and revenues from animal pollinated crops. Knowing the distribution of pollinators is therefore crucial for estimating their availability to pollinate crops; however, in general, we have an incomplete knowledge of where these pollinators occur. We propose a method to predict geographical patterns of pollination service to crops, novel in two elements: the use of pollinator records rather than expert knowledge to predict pollinator occurrence, and the inclusion of the managed pollinator supply. We integrated a maximum entropy species distribution model (SDM) with an existing pollination service model (PSM) to derive the availability of pollinators for crop pollination. We used nation-wide records of wild and managed pollinators (honey bees) as well as agricultural data from Great Britain. We first calibrated the SDM on a representative sample of bee and hoverfly crop pollinator species, evaluating the effects of different settings on model performance and on its capacity to identify the most important predictors. The importance of the different predictors was better resolved by SDM derived from simpler functions, with consistent results for bees and hoverflies. We then used the species distributions from the calibrated model to predict pollination service of wild and managed pollinators, using field beans as a test case. The PSM allowed us to spatially characterize the contribution of wild and managed pollinators and also identify areas potentially vulnerable to low pollination service provision, which can help direct local scale interventions. This approach can be extended to investigate geographical mismatches between crop pollination demand and the availability of pollinators, resulting from environmental change or policy scenarios.