Abstract: A vital trait in insects is their cuticular hydrocarbon (CHC) profile, which protects the insect against desiccation and serves in chemical communication. Due to these functions, CHC profiles are shaped by both climatic conditions and biotic interactions. Here, we investigated CHC differentiation in the neotropical parabiotic ant species Crematogaster levior and Camponotus femoratus, which mutualistically share a nest. Both consist of two cryptic species each (Cr. levior A and B and Ca. femoratus PAT and PS) that differ genetically and possess strongly different CHC profiles. We characterized and compared CHC profiles of the four cryptic species in detail. Our results suggest that Cr. levior A, Ca. femoratus PAT and Ca. femoratus PS adapted their CHC profiles to the parabiotic lifestyle by producing longer-chain CHCs. At the same time, they changed their major CHC classes, and produce more alkadienes and methyl-branched alkenes compared to Cr. levior B or non-parabiotic species. The CHC profiles of Cr. levior B were more similar to related, non-parabiotic species of the Orthocrema clade than Cr. levior A, and the chain lengths of B were similar to the reconstructed ancestral state. Signals of both the parabiotic partner (biotic conditions) and climate (abiotic conditions) were found in the CHC profiles of all four cryptic species. Our data suggest that mutualisms shaped the CHC profiles of the studied species, in particular chain length and CHC class composition. Beside this, signals of the parabiotic partners indicate potential impacts of biotic interactions, via chemical mimicry or chemical camouflage.

Abstract: Ripley’s K function is the classical tool to characterize the spatial structure of point patterns. It is widely used in vegetation studies. Testing its values against a null hypothesis usually relies on Monte-Carlo simulations since little is known about its distribution.
We introduce a statistical test against complete spatial randomness (CSR). The test returns the p-value to reject the null hypothesis of independence between point locations. It is more rigorous and faster than classical Monte-Carlo simulations. We show how to apply it to a tropical forest plot. The necessary R code is provided.

Abstract: The Amazon basin harbors a diverse ecological community that has a critical role in the maintenance of the biosphere. Although plant and animal communities have received much attention, basic information is lacking for fungal or prokaryotic communities. This is despite the fact that recent ecological studies have suggested a prominent role for interactions with soil fungi in structuring the diversity and abundance of tropical rainforest trees. In this study, we characterize soil fungal communities across three major tropical forest types in the western Amazon basin (terra firme, seasonally flooded and white sand) using 454 pyrosequencing. Using these data, we examine the relationship between fungal diversity and tree species richness, and between fungal community composition and tree species composition, soil environment and spatial proximity. We find that the fungal community in these ecosystems is diverse, with high degrees of spatial variability related to forest type. We also find strong correlations between α- and β-diversity of soil fungi and trees. Both fungal and plant community β-diversity were also correlated with differences in environmental conditions. The correlation between plant and fungal richness was stronger in fungal lineages known for biotrophic strategies (for example, pathogens, mycorrhizas) compared with a lineage known primarily for saprotrophy (yeasts), suggesting that this coupling is, at least in part, due to direct plant-fungal interactions. These data provide a much-needed look at an understudied dimension of the biota in an important ecosystem and supports the hypothesis that fungal communities are involved in the regulation of tropical tree diversity. © 2013 International Society for Microbial Ecology.

Abstract: Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (>150 Mg/ha, and >300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean >300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter- and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R2 = 0.54, RMSE = 48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain “wall-to-wall” AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ∼50 Mg/ha and R2 = 0.66 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values. © 2016 Elsevier B.V.

Abstract: In this study, the corrosion of S355 carbon steel was monitored for 50 days in equatorial environment. The experiments were conducted under three complementary approaches in a natural environment: (i) bacterial diversity was assessed with Miseq sequencing, (ii) observations of the surface and identifications of deposit compounds were realised with scanning electron microscopy (SEM), Raman spectrometry and EDX analyses, (iii) electrochemical measurements were used to calculate corrosion rate and to identify the phenomena which control corrosion. The results showed that the bacterial population had changed over immersion time from a dominance of β-proteobacteria to α-proteobacteria. This evolution decreased charge transfer resistance at the metal/deposit from 41.6 to 20.8 Ω/cm2 in 50 days, caused an increase in the corrosion rate by a factor of 2, from 0.13 to 0.27 mm/year. Surface analyses showed a progressive appearance of MIC markers in the deposit such as iron sulphide and manganese oxides that were detected after 30 days of immersion in the natural environment. In conclusion, this study describes in detail, the first stage of MIC activity on carbon steel surface in an equatorial brackish water, under aerobic conditions. © 2016 Elsevier Ltd.