Using gap-crossing capacity to evaluate functional connectivity of two Atlantic rainforest birds and their response to fragmentation

One of the main consequences of habitat loss and fragmentation is the increase in patch isolation and the consequent decrease in landscape connectivity. In this context, species persistence depends on their responses to this new landscape configuration, particularly on their capacity to move through the interhabitat matrix. Here, we aimed first to determine gap-crossing probabilities related to different gap widths for two forest birds (Thamnophilus caerulescens, Thamnophilidae, and Basileuterus culicivorus, Parulidae) from the Brazilian Atlantic rainforest. These values were defined with a playback technique and then used in analyses based on graph theory to determine functional connections among forest patches. Both species were capable of crossing forest gaps between patches, and these movements were related to gap width. The probability of crossing 40 m gaps was 50% for both species. This probability falls to 10% when the gaps are 60 m (for B. culicivorus) or 80 m (for T caerulescens). Actually, birds responded to stimulation about two times more distant inside forest trials (control) than in gap-crossing trials. Models that included gap-crossing capacity improved the explanatory power of species abundance variation in comparison to strictly structural models based merely on patch area and distance measurements. These results highlighted that even very simple functional connectivity measurements related to gap-crossing capacity can improve the understanding of the effect of habitat fragmentation on bird occurrence and abundance.

Using metrics from complex networks to evaluate machine translation

Establishing metrics to assess machine translation (MT) systems automatically is now crucial owing to the widespread use of MT over the web. In this study we show that such evaluation can be done by modeling text as complex networks. Specifically, we extend our previous work by employing additional metrics of complex networks, whose results were used as input for machine learning methods and allowed MT texts of distinct qualities to be distinguished. Also shown is that the node-to-node mapping between source and target texts (English-Portuguese and Spanish-Portuguese pairs) can be improved by adding further hierarchical levels for the metrics out-degree, in-degree, hierarchical common degree, cluster coefficient, inter-ring degree, intra-ring degree and convergence ratio. The results presented here amount to a proof-of-principle that the possible capturing of a wider context with the hierarchical levels may be combined with machine learning methods to yield an approach for assessing the quality of MT systems. (C) 2010 Elsevier B.V. All rights reserved.