Aim The nestedness temperature of presence-absence matrices is currently calculated with the nestedness temperature calculator (NTC). In the algorithm implemented by the NTC: (1) the line of perfect order is not uniquely defined, (2) rows and columns are reordered in such a way that the packed matrix is not the one with the lowest temperature, and (3) the null model used to determine the probabilities of finding random matrices with the same or lower temperature is not adequate for most applications. We develop a new algorithm, BINMATNEST (binary matrix nestedness temperature calculator), that overcomes these difficulties.Methods BINMATNEST implements a line of perfect order that is uniquely defined, uses genetic algorithms to determine the reordering of rows and columns that leads to minimum matrix temperature, and provides three alternative null models to calculate the statistical significance of matrix temperature. ResultsThe NTC performs poorly when the input matrix has checkerboard patterns. The more efficient packing of BINMATNEST translates into matrix temperatures that are lower than those computed with the NTC. The null model implemented in the NTC is associated with a large frequency of type I error, while the other null models implemented in BINMATNEST (null models 2 and 3) are conservative. Overall, null model 3 provides the best performance. The nestedness temperature of a matrix is affected by its size and fill, but the probability that such a temperature is obtained by chance is not. BINMATNEST reorders the input matrix in such a way that, if fragment size/isolation plays a role in determining community structure, there will be a significant rank correlation between the size/ isolation of the fragments and the way that they are ordered in the packed matrix. Main conclusionsThe nestedness temperature of presence-absence matrices should not be calculated with the NTC. The algorithm implemented by BINMATNEST is more robust, allowing for across-study comparisons of the extent to which the nestedness of communities departs from randomness. The sequence in which BINMATNEST reorders habitat fragments provides information about the causal role of immigration and extinction in shaping the community under study.

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Recent attempts to examine the biological processes responsible for the general characteristics of mutualistic networks focus on two types of explanations: nonmatching biological attributes of species that prevent the occurrence of certain interactions (“forbidden links”), arising from trait complementarity in mutualist networks (as compared to barriers to exploitation in antagonistic ones), and random interactions among individuals that are proportional to their abundances in the observed community (“neutrality hypothesis”). We explored the consequences that simple linkage rules based on the first two hypotheses (complementarity of traits versus barriers to exploitation) had on the topology of plant–pollination networks. Independent of the linkage rules used, the inclusion of a small set of traits (two to four) sufficed to account for the complex topological patterns observed in real-world networks. Optimal performance was achieved by a “mixed model” that combined rules that link plants and pollinators whose trait ranges overlap (“complementarity models”) and rules that link pollinators to flowers whose traits are below a pollinator-specific barrier value (“barrier models”). Deterrence of floral parasites (barrier model) is therefore at least as important as increasing pollination efficiency (complementarity model) in the evolutionary shaping of plant–pollinator networks.

Bright yellow and red colors in animals and plants are assumed to be caused by carotenoids (CAR). In animals, these pigments are deposited in scales, skin and feathers. Together with other naturally occurring and colorless substances such as melatonin and vitamins, they are considered antioxidants due to their free-radical-scavenging properties. However, it would be better to refer to them as "antiradicals", an action that can take place either donating or accepting electrons. In this work we present quantum chemical calculations for several CAR and some colorless antioxidants, such as melatonin and vitamins A, C and E. The antiradical capacity of these substances is determined using vertical ionization energy (I), electron affinity (A), the electrodonating power (omega(-)) and the electroaccepting power (omega(+)). Using fluor and sodium as references, electron acceptance (R(a)) and electron donation (R(d)) indexes are defined. A plot of R(d) vs R(a) provides a donator acceptor map (DAM) useful to classify any substance regarding its electron donating-accepting capability. Using this DAM, a qualitative comparison among all the studied compounds is presented. According to R(d) values, vitamin E is the most effective antiradical in terms of its electron donor capacity, while the most effective antiradical in terms of its electron acceptor capacity, R(a), is astaxanthin, the reddest CAR. These results may be helpful for understanding the role played by naturally occurring pigments, acting as radical scavengers either donating or accepting electrons.

Are bird-pollinated flowers red because bees - which might rob the flower of its nectar - cannot easily detect them, or might it be because of more subtle evolutionary trade-offs?