Extrinsic Summarization Evaluation: A Decision Audit Task

Abstract. In this work we describe a large-scale extrinsic evaluation of automatic speech summarization technologies for meeting speech. The particular task is a decision audit, wherein a user must satisfy a complex information need, navigating several meetings in order to gain an understanding of how and why a given decision was made. We compare the usefulness of extractive and abstractive technologies in satisfying this information need, and assess the impact of automatic speech recognition (ASR) errors on user performance. We employ several evaluation methods for participant performance, including post-questionnaire data, human subjective and objective judgments, and an analysis of participant browsing behaviour.

Ungulate browsing shapes climate change impacts on forest biodiversity in Hungary

Climate change can result in a slow disappearance of forests dominated by less drought-tolerant native European beech (Fagus sylvatica) and oak species (Quercus spp.) and further area expansion of more drought-tolerant non-native black locust (Robinia pseudoacacia) against those species in Hungary. We assumed that the shift in plant species composition was modified by selective ungulate browsing. Thus, we investigated which woody species are selected by browsing game. We have collected data on the species composition of the understory and the browsing impact on it in five different Hungarian even-aged forests between 2003 and 2005. Based on these investigations the non-native Robinia pseudoacacialiving under more favourable climatic conditions was generally preferred (Jacobs’ selectivity index: D=0.04±0.77), while the nativeFagus sylvatica and Quercus spp. (Q. petraea, Q. robur), both more vulnerable to increasing aridity, were avoided (D=-0.37±0.11;-0.79±0.56;-0.9±0.16; respectively) among target tree species. However, economically less or not relevant species, e.g. elderberry (Sambucus spp.), blackberry (Rubus spp.) or common dogwood (Cornus sanguinea) were the most preferred species (D=0.01±0.71; -0.12±0.58; -0.2±0.78, respectively). Our results imply that biodiversity conservation, i.e. maintaining or establishing a multi-species understory layer, can be a good solution to reduce the additional negative game impact on native target tree species suffering from drought. Due to preference for Robinia pseudoacaciaselective browsing can decelerate the penetration of this species into native forest habitats. We have to consider the herbivorous pressure of ungulates and their feeding preferences in planning our future multifunctional forests in the light of climate change impacts

An integrated, spatio-temporal modelling framework for analysing biological invasions

Aim: We develop a novel modelling framework for analysing the spatio-temporal spread of biological invasions. The framework integrates different invasion drivers and disentangles their roles in determining observed invasion patterns by fitting models to historical distribution data. As a case study application, we analyse the spread of common ragweed (Ambrosia artemisiifolia). Location: Central Europe. Methods: A lattice system represents actual landscapes with environmental heterogeneity. Modelling covers the spatio-temporal invasion sequence in this grid and integrates the effects of environmental conditions on local invasion suitability, the role of invaded cells and spatially implicit “background” introductions as propagule sources, within-cell invasion level bulk-up and multiple dispersal means. A modular framework design facilitates flexible numerical representation of the modelled invasion processes and customization of the model complexity. We used the framework to build and contrast increasingly complex models, and fitted them using a Bayesian inference approach with parameters estimated by Markov chain Monte Carlo (MCMC). Results: All modelled invasion drivers codetermined the A. artemisiifolia invasion pattern. Inferences about individual drivers depended on which processes were modelled concurrently, and hence changed both quantitatively and qualitatively between models. Among others, the roles of environmental variables were assessed substantially differently subject to whether models included explicit source-recipient cell relationships, spatio-temporal variability in source cell strength and human-mediated dispersal means. The largest fit improvements were found by integrating filtering effects of the environment and spatio-temporal availability of propagule sources. Main conclusions: Our modelling framework provides a straightforward means to build integrated invasion models and address hypotheses about the roles and mutual relationships of different putative invasion drivers. Its statistical nature and generic design make it suitable for studying many observed invasions. For efficient invasion modelling, it is important to represent changes in spatio-temporal propagule supply by explicitly tracking the species’ colonization sequence and establishment of new populations

Accounting for imperfect observation and estimating true species distributions in modelling biological invasions

The documentation of biological invasions is often incomplete with records lagging behind the species’ actual spread to a spatio-temporally heterogeneous extent. Such imperfect observation bears the risk of underestimating the already realised distribution of the invading species, misguiding management efforts and misjudging potential future impacts. In this paper, we develop a hierarchical modelling framework which disentangles the determinants of the invasion and observation processes, models spatio-temporal heterogeneity in detection patterns, and infers the actual, yet partly undocumented distribution of the species at any particular time. We illustrate the model with a case study application to the invasion of common ragweed Ambrosia artemisiifolia in Austria. The invasion part of the model reconstructs the historical spread of this species across a grid of ∼ 6x6 km2 cells as driven by spatio-temporal variation in physical site conditions, propagule production, dispersal, and ‘background’ introductions from unknown sources. The observation part models the detection of the species’ occurrences based on heterogeneous sampling efforts, human population density, and estimated local invasion level. We fitted the hierarchical model using a Bayesian inference approach with parameters estimated by Markov chain Monte Carlo (MCMC). The actual spread of A. artemisiifolia concentrated on the climatically well-suited lowlands and was mainly driven by spatio-temporal propagule pressure from source cells with long-distance dispersal occurring rather frequently. Annual detection probabilities were estimated to vary between about 1 and up to 28%, depending mainly on sampling intensity. The model suggested that by 2005 about half of the actual distribution of the species was not yet documented. Our hierarchical model offers a flexible means to account for imperfect observation and spatio-temporal variability in detection efficiency. Inferences can be used to disentangle aspects of the invasion dynamics itself from patterns of data collection, develop improved future surveying schemes, and design more efficient invasion management strategies

Modelling the spread of ragweed: Effects of habitat, climate change and diffusion

Ragweed (Ambrosia artemisiifolia L.) is an annual plant native in North America which has been invading Central Europe for 150 years. Caused by the warming of the European climate its spread process has accelerated in the last few decades. The pollen of ragweed evokes heavy allergies and – what probably counts even more – because of its bloom rather late in summer causes a second wave of allergy when other pollen allergies have decayed. We have reconstructed the invasion process of ragweed in Austria by collecting all records until the year 2005. Austria was subdivided into more than 2600 grid cells of ≈$35\,\text{km}^2$ each. Ragweed records were related to environmental descriptors (average temperatures, land use, etc.) by means of logistic regression models, and the suitability of grid cells as habitat for ragweed was determined. This enabled modelling of the diffusive spread of ragweed from 1990 to 2005. The results of the simulations were compared with the observed data, and thus the model was optimised. We then incorporated regional climate change models, in particular increased July mean temperatures of +2.3\,^\circ\text{C} in 2050, increasing considerably future habitat suitability. This is used for predicting the drastic dispersal of ragweed during the forthcoming decades

Reply to Keller and Springborn: No doubt about invasion debt

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